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Institutul Naţional de Statistică National Institute of Statistics INSTITUTUL NAŢIONAL DE STATISTICĂ Revista Română de Statistică B-dul Libertăţii, nr. 16, sector 5, Bucureşti Telefon/fax: 0213171110 e-mail: [email protected] www.revistadestatistică.ro/supliment ISSN 2359 – 8972 Revista Română de Statistică Supliment Romanian Statistical Review Supplement Simpozionul Ştiinţific Internaţional “România pe calea redresării. Decizii economico-financiare luate sub risc” 10-11 decembrie 2015 1/2016 www.revistadestatistică.ro/supliment REVISTA ROMÂNĂ DE STATISTICĂ SUPLIMENT SUMAR / CONTENTS 1/2016 USING THE DYNAMIC MODEL ARMA TO FORECAST THE MACROECONOMIC EVOLUTION Prof. Constantin ANGHELACHE PhD., Prof. Janusz GRABARA PhD., Assoc. prof. Alexandru MANOLE PhD. 3 STATISTIC INDICATORS ON THE RELATIONSHIP BETWEEN ECONOMY AND FOREIGN TRADE OF THE REPUBLIC OF MOLDOVA (INCLUDING WITH ROMANIA) DURING 2003- 2014 14 Prof. Ioan PARTACHI PhD., Senior Lecturer Natalia ENACHI USING THE AUTOREGRESSIVE MODEL FOR THE ECONOMIC FORECAST DURING THE PERIOD 2014- 2018 21 Prof. Constantin ANGHELACHE PhD., Prof. Ioan Constantin DIMA PhD., Lect. Mădălina-Gabriela ANGHEL PhD. MANAGING FINANCIAL INSTRUMENTS BY DEVELOPMENT BANK OF ROMANIA 32 Prof. Vergil VOINEAGU PhD., Prof. Michal BALOG, PhD., Daniel DUMITRESCU PhD. Student, Diana SOARE (DUMITRESCU) PhD Student ESSENTIAL ASPECTS REGARDING THE OPTIMAL PREVENTION 38 Prof. Constantin ANGHELACHE PhD., Prof. Mario G.R. PAGLIACCI PhD. Emilia STANCIU PhD. Student, Cristina SACALĂ PhD. Student SPECIFIC ELEMENTS OF CORRELATION BETWEEN INFOMATION AND RISK 43 Assoc. prof. Alexandru MANOLE PhD., Emilia STANCIU PhD. Student . Alexandru URSACHE PhD. Student MODEL OF STATIC PORTFOLIO CHOICES Lect. Mădălina Gabriela ANGHEL PhD., Gyorgy BODO Phd. Student. Okwiet BARTEK, PhD. Student 49 MODEL OF STATIC PORTFOLIO CHOICES IN AN ARROW-DEBREU ECONOMY 54 Prof. Gabriela Victoria ANGHELACHE PhD., Lect. Mădălina Gabriela ANGHEL PhD., Gyorgy BODO Phd. Student www.revistadestatistica.ro/supliment Revista Română de Statistică - Supliment nr. 1 / 2016 PORTFOLIO MANAGEMENT AND PREDICTABILITY 59 Prof. Gabriela Victoria ANGHELACHE PhD., Prof. Vladimir MODRAK, PhD , Lect. Mădălina Gabriela ANGHEL PhD., Marius POPOVICI PhD. Student SIGNIFICANT ASPECTS OF INVESTMENT DYNAMICS 64 Prof. Gabriela Victoria ANGHELACHE PhD., Lect. Mădălina Gabriela ANGHEL PhD., Marius POPOVICI PhD. Student MODEL FOR ESTIMATING FINANCIAL RESULTS AT COMPANY LEVEL BY USING SIMPLE LINEAR REGRESSION Andreea-Gabriela BALTAC PhD. Student Zoica DINCA (NICOLA) PhD. Student RISK AVERSION AND INDIVIDUAL PREFERENCES MODELLING Prof. Constantin ANGHELACHE PhD., Lecturer Mădălina Gabriela ANGHEL PhD., Assoc. prof. Aurelian DIACONU PhD. 70 77 THE MAJOR MACROECONOMIC EVOLUTIONS BY THE END OF JULY 2015 83 Prof. Constantin ANGHELACHE PhD., Alexandru URSACHE PhD. Student THE GROSS DOMESTIC PRODUCT EVOLUTION IN ROMANIA Assoc. prof. Alexandru MANOLE PhD., Prof. Sebastian KOT, PhD, Marius POPOVICI PhD. Student, Georgiana NIŢĂ PhD. Student 87 THE INFLATION (CONSUMER PRICES) IN THE ROMANIAN ECONOMY 96 Prof. Constantin ANGHELACHE PhD. Georgiana NIŢĂ PhD. Student, Alexandru BADIU PhD. Student THE MAIN ASPECTS REGARDING THE DYNAMICS OF THE INDUSTRIAL PRODUCTION INDICES 100 Prof. Constantin ANGHELACHE PhD., Assoc. prof. Aurelian DIACONU PhD., Cristina SACALĂ PhD. Student PRODUCTION OF SERVICES DURING THE LAST YEAR 104 Prof. Constantin ANGHELACHE PhD., Prof. Radu Titus MARINESCU PhD., Assoc. prof. Aurelian DIACONU PhD. FINANCIAL INCLUSION, FOCUS ON ROMANIAN MIGRANTS AND THEIR FAMILIES 110 Prof. Constantin ANGHELACHE PhD., Olivia Georgiana NITA PhD. Student, Alexandru BADIU PhD. Student 2 Romanian Statistical Review - Supplement nr. 1 / 2016 Using the Dynamic Model ARMA to Forecast the Macroeconomic Evolution Prof. Constantin ANGHELACHE PhD. Bucharest University of Economic Studies “Artifex” University of Bucharest Prof. Janusz GRABARA PhD. Czestochowa University of Technology Assoc. prof. Alexandru MANOLE PhD. “Artifex” University of Bucharest Abstract The ARMA models, provide in the statistical analysis of time series, a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the auto-regression and the second for the moving average. We want to estimate, by using the informatics soft Eviews, the future evolutions of the gross domestic product in Romania, for the period 2009 -2013, obtaining thus, on the ground of the official evolutions during the period 1991 - 2008, a model AR meant to grasp ex post the evolution of the economic growth from our country, for the period 2009 -2013. To achieve ex post the forecast for the evolution of the GDP, during the period 2009 -2013, we will use as “sample” the data published in the interval 1991 -2008. Key words: dynamic, model ARMA, validity, prediction, autoregressive model, correlation, macroeconomic evolution Introduction In this paper we shall elaborate an autoregressive model of prediction ARMA, utilizing at first stage the data published by UNCTAD, for the variables gross domestic product (GDP), flow of direct foreign investments (DFI), respectively final consumption (FC) in the case of Romania , during the statistical period 1991 -2008. Through the informatics soft Eviews, we undertake to achieve the estimation of the future evolutions of the gross domestic product in Romania , for the period 2009 -2013, obtaining thus, on the ground of the official evolutions during the period 1991 - 2008, a model AR meant to grasp ex post the evolution of the economic growth from our country, for the period 2009 -2013. This model is developed as a first precursory stage, which undertakes to validate the prediction model. Taking into consideration the above submitted aspects, we shall continue our steps meant to build up an autoregressive model of prediction. In this respect, we shall utilize the data Revista Română de Statistică - Supliment nr. 1 / 2016 3 basis published UNCTAD, which is grasping the evolution of the variables which are going to be used in the model construction, respectively: variables gross domestic product (GDP), flow of direct foreign investments, respectively final consumption ((defined in the frame of the model as FC) of the statistical interval 1991 - 2013. Thus, we shall use as “sample” the data published in the interval 1991 -2008, in order to achieve ex post the forecast for the evolution of the GDP, during the period 2009 -2013. Testing the validity of the prediction model ARMA Through the Model 1, which defines EQ01, a first step as to building up the prediction model has been accomplished. Out of the statistical tests utilized for validating the econometric, we observe that both the autoregressive component GDP(-1), and the residual variable “C”, which grasps the influence of those factors not included in the frame of the model, and mainly the dependent variable final consumption (FC), have a significant contribution to the estimation of the economic growth at the level of Romania , which is underlined by the outcomes of the probability of each dependent variable, included in the frame of the model. Meantime, the value R-squared is indicating the sturdiness of the built up model, emphasizing as well its validity in proportion of 99.88%. The high percentage of validity of the built up model is completed by the result of the test Prob (F-statistic), corroborated with the result of the statistical test Adjusted R-squared, which confirms once more the correctness of the developed model (Model 1). The eequation of the built up model (Table 1 - Model 1) is of the form: (EQ01) GDP = 1.109218*FC - 0.683974*DFI + 0.183799*GDP (-1) - 3319.143 Out of the equation (EQ01) of the developed model (Model 1), based on both the values of the coefficients of the econometric model, and on the interpretation of the probabilities related to these coefficients, previously taken into account, we observe that the variable final consumption has a very high significance degree as far as inducing the evolution of GDP of our country is concerned followed, from the point of view of the significance in describing the evolution of GDP, by the variable flow of direct foreign investments and the autoregressive variable GDP on the lag 1 (GDP(-l)). To note also the value taken by the free term C, (-3319.143), which grasps the impact or the negative influence on the evolution of the gross domestic product, of the variables not taken into consideration and implicitly not-comprised in the construction of the prediction model. 4 Romanian Statistical Review - Supplement nr. 1 / 2016 We shall continue with the test for the existence of the serial correlation in the frame of the model represented by the equation EQ01, by applying the test “Breusch-Godfrey Serial Correlation LM Test”, (Table 2), approach achieved with the help of the informatics soft Eviews 7.2. Thus, following the previously statements, we observe that the value of the probability Chi-Square, on the testing lag 1 counts for 0.0247, an outcome implying the acceptance of the null hypothesis alleging that : « There is a serial correlation on the lag 1 ». Model 1. The model defining the first equation noted as EQ01 Table 1 Dependent Variable: GDP Method: Least Squares Date: 07/26/15 Time: 16:56 Sample (adjusted): 1992 2008 Included observations: 17 after adjustments Variable DFI FC C GDP(-r: R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Coefficient -0.683974 1.109218 -3319.143 0.183799 0.998845 0.998578 2002.360 52122793 -151.0771 3747.330 0.000000 Std. Error t-Statistic 0.439632 -1 5557S6 0.078647 14103Ă0 1285.126 -2.582737 0.076792 2.393466 Mean dependent var S.D. dependent var Akaike info criterion Schwa rz criterion Hannan-Quinn enter. Durbin-Watson stat Prob. 0.1438 0.0000 0.0227 0.0325 6614442 53107.36 18.24436 18.44042 18.26385 0.948488 The fact that the outcomes of the statistical test “Breusch-Godfrey Serial Correlation LM Test” confirmed the existence of the serial correlation in the frame of the developed model, will make us achieve a parallel estimation of the evolution of the gross domestic product at the level of our country, but this time we shall utilize the component AR , which eliminates the serial correlation from the statistical series subject of the prediction, the model thus obtained having normally a bigger power of prediction, consequently a higher accuracy of the economic growth forecast. Revista Română de Statistică - Supliment nr. 1 / 2016 5 The test Breusch-Godfrey for testing the serial correlation for the autoregressive model (EQ01) Table 2 Breusch-Godfrey Serial Correlation LM Test: F-statistic 5.058960 Prob. F{1,12> Obs*R-squared 5.041475 Prob. Chi-Square(l) Test Equation: Dependent Variable: RESID 0.0441 0.0247 Method: Least Squares Date: 07/26/15 Time: 17:05 Sample: 1992 2008 Included observations: 17 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic DFI 0.283234 0.403914 0.701224 FC -0.033152 0.070220 -0.472116 C 354.2602 1132.869 0.312711 GDP(-1) 0.009891 0.067181 0.147223 RESIDf-1) 0.594914 0.264499 2.249213 R-squared 0.296557 Mean dependentvar Adjusted R-squared 0.062076 S.D. dependent var S.E. of regression 1747.984 Akaike Info criterion Sum squared resid 36665395 Schwa rz criterion Log likelihood -148.0871 Hannan-Qulnn enter. F-statistic 1.264740 Durbin-Watson stat Prob(F-statistlc) 0.336755 Prob. 0.4965 0.6453 0.7599 0.8854 0.0441 4.87E-12 1804.903 13.01024 18.25531 18.03460 1.731636 Referring to the econometric model which submit the serial correlation, previously represented through EQ01 (Model 1), through the forecasting function provided in the frame of Eviews 7.2, for the autoregressive models, we achieved the representation (Table 3), which emphasize ex post the prediction of the GDP for the period 2009 - 2013. Thus, according to the previous discussion, the implementation of this prediction on the evolution of the GDP, of the period 2009 - 2013, bears a purely theoretical role as to finalizing and validating the prediction model which will be used for the forecast of the GDP for the period 2014 -2018, at the level of Romania . 6 Romanian Statistical Review - Supplement nr. 1 / 2016 The representation of the prediction for the GDP {Model 1), during the period 2009 -2013, in Romania Table 3 Out of the test achieved for the forecast of the period 2009 - 2013, evidenced by the equation EQ01, we observe that the value “RootMean Squared Error”, is grasping a prediction error of 9035.72, between the real values of the evolution of the GDP and the forecasted ones. In order to improve the prediction achieved through the equation EQ01, resulting out of the ModelOl, and to increase the accuracy level of the prognosis achieved for the evolution of the gross domestic product, we shall remove the serial correlation, by including the component AR(1), which would improve the quality of autoregressive model. Thus, by including the component AR(1) in the frame of the model previously set up (Model 1), according to the outcomes of the statistical tests generated by the data processing with the help of the informatics soft Eviews 7.2, we remark a higher qualitative level, which will generate a higher accuracy of the prognosis, very close to the real values recorded by evolution of the GDP, during the period 2009 -2013, getting thus a model which may be applied in practice in the forecasting processes performed by the institutions in charge. The autoregressive model The high validity of the developed model (Table 4 - Model 2) is underlined by the tests R-squared and Adjusted R-squared, which confirm the correctness of the model, in proportion of 99.92%, respectively 99.90%. Meantime the value of the test Prob(F-statistic), is confirming the accuracy of the developed model. Revista Română de Statistică - Supliment nr. 1 / 2016 7 Model 2. The autoregressive model which defines the 2nd equation EQ02 Table 4 Dependent Variable: FIB Method: Least Squares Date: 07/26/15 Time: 17:04 Sample (adjusted): 1993 2008 Included observations: 16 after adjustments Convergence achieved after 10 iterations Variable DFI FC C PIFJ(-1) AR{1) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistlc Pro b[F-stati stlc) Inverted AR Roots Coefficient -0.164772 1.153688 -3873.434 0.106970 0.744597 0.999283 0.999022 1673.224 30796475 -138.4656 3831.418 0.000000 .74 Sid. Error t-Statistic 0.309899 -0 531697 0.057659 20.00877 3189.998 -1.214243 0.059576 1.795522 0.233209 3.192828 Mean dependentvar S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbln-Watson stat Prob. 0.6055 0.0000 0.2501 0.1001 0.0086 68980.97 53502.50 17.93320 13.17463 17.94556 1.298047 Another Un alt criterion making us to anticipate that the second developed model (Model 2), is more performing in order to estimate the evolution of the economic growth, is given by a cumulus of statistical criteria, such as Akaike, Schwarz and Hannan-Quinn. Although, when comparing the two models built up we observe that the value of the test Schwarz, does not change, the tests Akaike and Hannan-Quinn are underlining better outcomes for the second model, which contains the component AR(1). The new developed model, Model 2 is represented through the equation: GDP = 1.15368*FC - 0.16477*DFI + 0.10697*GDP (-1) + 0.74459*AR(1) - 3873.43 (EQ02) As already previously mentioned, by including the component AR(1), in the frame of the developed model (Table 4 - Model 2), the serial correlation will be removed generating thus, a considerable improvement of the quality of the prediction on the economic growth in Romania . Further on , we shall test whether the new proposed model is still submit serial correlation, applying in this respect the method “Breusch-Godfrey Serial Correlation LM Test”, as proceeded previously. According to the outcomes of the test “Breusch-Godfrey Serial Correlation LM Test”, (Table 5), we can deduce that the new proposed 8 Romanian Statistical Review - Supplement nr. 1 / 2016 model of prediction does not submit the serial correlation any more. This interpretation is confirmed by the percentage of 25.35% of the probability “Chi- Square” which, according to the statistical methodology, is rejecting the presumed null hypothesis, according to which « There is serial correlation on the lag 1 ». Table 5. The test Breusch-Godfrey for testing the serial correlation for the model AR (EQ02) Breusch-Godfrey Serial Correlation LM Test F-statistlc 0.887300 Prob. F£1,10> Obs*R-squared 1.303978 Prob. Chi-Square(l) Test Equation: Dependent Variable: RESID Method: Least Squares Date: 07/26/15 Time: 17:23 Sample: 1993 2008 Included observations: 16 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic DFI 0.117757 0.335649 0.350835 FC -0.005331 0.058232 -0.091540 C 1628.094 3642.580 0.446962 GDP(-1) -0.015195 0.062018 -0.245008 AR(1) -0.210139 0.323602 -0.649376 RESIDf-1) 0.450249 0.477989 0.941966 R-squared 0.081499 Mean dependent var Adjusted R-squared -0.377752 S.D. dependent var S.E. of regression 1681.862 Akaike info criterion Sum squared resid 28286604 Schwarz criterion Log likelihood -137.7855 Hannan-Quinn enter. F-statistlc 0.177460 Durbin-Watson stat Prob(F-statistic) 0.964931 0.3684 0.2535 Prob. 0.7330 0.9289 0.6644 0.8114 0.5307 0.3684 -1.53E-07 1432.864 17.97319 18.26291 17.98302 1.592751 According to the tests achieved for the simulation of the prognosis for the period 2009 - 2013, (Table 6), evidenced by the equation EQ02, we observe that the value “Root Mean Squared Error” is grasping a prediction error much smaller than the one underlined by the equation EQ01, previously interpreted. Thus, the prediction error or the difference between the real values recorded by the evolution of the GDP, during the period 2009 -2013 levels up to 3 570.03 as against those predicted through the first equation. Revista Română de Statistică - Supliment nr. 1 / 2016 9 The representation of the prediction for the GDP (Model 2), during the period 2009 -2013, in Romania Table 6 If analysing comparatively he estimation achieved through the two models built up, we observe that the model representing the component AR on the lag 1, which eliminates the serial correlation of the data basis utilised for the prediction of the GDP, at the level of Romania is more performing, having a higher accuracy in respect of the economic growth forecast. The fact that the second equation EQ02, which grasps the second developed model, Model 2 is valid from the statistical point of view for the prediction of the evolution of the GDP, on the basis of the data recorded at the level of our country for the dependent variables dependent: final consumption (FC) and flow of direct foreign investments (DFI), is confirmed by the outcomes obtained as a result of the model implementation. Out of the table submitted in the Table 7, resulting by applying the models previously developed, on the basis of data which grasp both the evolution of the final consumption, and that of the flow of DFI, variables which are considerably influence the evolution of the economic growth in our country, we observe that the prediction obtained through the utilization EQ02, is showing a higher accuracy as against the evolution of the GDP predicted through EQ01. 10 Romanian Statistical Review - Supplement nr. 1 / 2016 The parallel representation of the outcomes expost the predictions grasped by EQ01 vs EQ02 for the period 2009 -2013 Table 7 obs 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 EQ01_PIBF NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 178175 .60 175200.1 186931.9 177976.1 190476.7 EQ02_PIBF NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 170754.00 167921.9 180082.9 169891.0 183945.9 GDP I 30593.07 20759.75 27951.94 31887.80 37618.60 36911.15 35616.63 41749.52 35995.56 37305.10 40585.89 45988.51 59466.02 75794.73 99172.61 122695.85 170616 95 204338.60 164344.38 164792.25 182610.67 169396 .06 186438.481 The fact that by utilizing the Modelul 2 and implicitly EQ02 we shall achieve a prediction of high accuracy is confirmed also by the graphical representation of the evolution of the GDP over the period 1991 -2013, parallel with the two estimations achieved for the period 2009 - 2013, through the models being utilized. Taking into account the above explanations and proposals, we observe that by utilizing a model of type AR in the prediction for the GDP, on the case of Romania, we get outcomes of high accuracy if considering the real evolutions. In this respect, we can deduce that the implementation of such a model for the prognosis on evolution of the GDP, for the period 2014-2018, based on the evolution of the variables final consumption (FC) and flow of direct foreign investments (DFI), during the period 1991 -2013, can be a successful one. In the graphical representation of the evolutions generated by the utilization of EQ01 and EQ02, (Table 8), the blue line is grasping the prognosis resulting from the implementation of EQ1, the red one evidencing the prognosis resulting out of the utilization of EQ02, while the evolution grasped by the green line is marking the real evolution of the GDP, according to the data bases of UNCTAD. Revista Română de Statistică - Supliment nr. 1 / 2016 11 The representation of the prediction on the GDP, through the two models represented by EQ01 vs EQ02 during the period 2009 -2013, in Romania Table 8 Conclusions The value R-squared is indicating the sturdiness of the built up model, emphasizing as well its validity in proportion of 99.88%. The high percentage of validity of the built up model is completed by the result of the test Prob (F-statistic), corroborated with the result of the statistical test Adjusted R-squared, which confirms once more the correctness of the developed model. The test for the existence of the serial correlation in the frame of the model represented by the equation EQ01, by applying the test “Breusch-Godfrey Serial Correlation LM Test”, (Table 2), approach achieved with the help of the informatics soft Eviews 7.2. and we observe that the value of the probability Chi-Square, on the testing lag 1 counts for 0.0247, an outcome implying the acceptance of the null hypothesis alleging that : « There is a serial correlation on the lag 1 ». The implementation of the prediction on the evolution of the GDP, of the period 2009 - 2013, bears a purely theoretical role as to finalizing and validating the prediction model which will be used for the forecast of the GDP for the period 2014 -2018, at the level of Romania. The autoregressive model is underlined by the tests R-squared and Adjusted R-squared, which confirm the correctness of the model, in proportion of 99.92%, respectively 99.90%. Meantime the value of the test Prob(F-statistic), is confirming the accuracy of the developed model. 12 Romanian Statistical Review - Supplement nr. 1 / 2016 The prediction error or the difference between the real values recorded by the evolution of the GDP, during the period 2009 -2013 levels up to 3 570.03 as against those predicted through the first equation. Using a model of type AR in the prediction for the GDP, on the case of Romania, we get outcomes of high accuracy if considering the real evolutions. In this respect, we can deduce that the implementation of such a model for the prognosis on evolution of the GDP, for the period 2014-2018, based on the evolution of the variables final consumption (FC) and flow of direct foreign investments (DFI), during the period 1991 -2013, can be a successful one. References 1. Andrei, T.; Bourbonais, R. (2008). Econometrics, Economica Publishing House, Bucharest 2. Anghel M.G. (2013). Modele de gestiune şi analiză a portofoliilor, Editura Economică, Bucureşti 3. Anghel M.G. et al. (2010). Analiza rentabilităţii portofoliilor de instrumente financiare cu ajutorul modelelor econometrice de tip ARMA, Revista Română de Statistică, Supliment, nr. 12 4. Anghel M.G. et al. (2010). Utilizarea seriilor cronologice în analiza rentabilităţii activelor financiare, Revista Română de Statistică, Supliment, nr. 3 5. Anghelache C., Anghel M.G., Manole A. (2015). Modelare economică, financiarbancară şi informatică, Editura Artifex 6. Anghelache C., Anghel M.G., (2015). Theoretical aspects concerning the use of the statistical-econometric instruments the analysis of the financial assets, Romanian Statistical Review - Supplement, No. 9 7. Anghelache, C.; Anghelache, C.S. (2008). Certain theorethical aspects regarding the use of the dynamic series for economic Analysis, Theorethical and Applied Economics, vol. 4 8. Bauer Gregory H., Vorkink Keith (2011). „Forecasting multivariate realized stock market volatility”, Journal of Econometrics 9. Benjamin, C.; Herrard, N.; Houée-Bigot, M.; Tavéra, C. (2012). Forecasting with an Econometric Model, Springer 10. He, C.; Silvennoinen, A.; Terasvirta, T. (2008). Parameterizing Unconditional Skewness in Models for Financial Time Series, CREATES Research Paper No. 2008-7 11. Ling, S.; Mcaleer, M. (2003) – Asymptotic Theory For A Vector ARMA-GARCH Model, Econometric Theory, 19, 280–310+ Printed in the United States of America 12. Tang, H.; Chiu, K.C.; Xu, L. (2003). Finite Mixture of ARMA-GARCH Model for Stock Price Prediction, Proc. Of 3rd International Workshop on Computational Intelligence in economics and Finance, North Carolina, USA 13. Wooldrige, J. (2006). Introductory econometrics. A modern approach – 2 edition, MIT Press Revista Română de Statistică - Supliment nr. 1 / 2016 13 Statistic Indicators on the Relationship between Economy and Foreign Trade of the Republic of Moldova (including with Romania) during 2003- 2014 Prof. Ioan PARTACHI PhD. Senior Lecturer Natalia ENACHI ([email protected]) Academy of Economic Studies, Chisinau, Republic of Moldova Abstract The basic component of the national economy system and the component of the foreign economic relations is – foreign trade. The foreign trade balance of the country can ensure the balance of the national economy, economic efficiency, increase of economic potential, benefit from the experience of economically advanced countries etc. So, the analysis of the interdependence between foreign trade and economy of a country is natural. Key words: international openness, annual advance coefficient, foreign trade contribution to GDP creation, coverage degree of imports by exports, coverage index of imports by exports, country’s participation intensity in the international exchange of goods. Over time foreign trade was considered the centre of each country’s economy. Thus, foreign trade area is considered not just a transaction of goods, but also a necessary element of the production process and the national economy as a whole. For the Republic of Moldova foreign trade plays a very important role, motivated by a small internal market, which limits the production development, insufficient quantity of raw material and energy sources needed to meet the exigencies of the national economy, both for intermediate consumption (production goods and services) and for the final consumption (private and government consumption, households), which caused high imports increase. The relationship between foreign trade and the national economy has always been discussed in the works of researchers given the important role of trade in economic growth of the country. Thus, the purpose of the investigations carried out in the paper is to analyse the statistical indicators that ground the investigation of this relationship. As additional information necessary for calculating and analysing the indicators that describe the relationship between foreign trade and Moldovan economy, may be presented in the following table: 14 Romanian Statistical Review - Supplement nr. 1 / 2016 Evolution of statistical indicators underlying the characterization of the relationship between foreign trade and Moldovan economy of during the years (2003 - 2014) Table 1 Indicators Gross Domestic Product (GDP), million MDL (current market prices) Average annual exchange official rates, in MDL for USD 1* Gross Domestic Product (GDP), milion USD The average annual number of resident population ( N p ), thousand persons 2003 2004 2005 2006 2007 2008 227619 332032 337652 444754 553430 662922 113,943 112,3283 112,600 113,1319 112,1362 110,3895 11980,9 22598,2 22988,3 33408,0 44402,5 66056,3 33612,9 33604,0 33595,2 33585,2 33577,0 33570,1 Indicators 2009 2010 2011 2012 2013 2014 Gross Domestic Product (GDP), 660430 771885 882349 888228 1100312 1111501 million MDL (current market prices) Average annual exchange official 111,1134 112,3663 111,737 112,1122 112,5907 114,0388 rates, in MDL for USD 1* Gross Domestic Product (GDP), 55437,6 55813,0 77016,2 77284,2 77967,2 77942,3 milion USD The average annual number of resident 33565,6 33562,1 33560,0 33559,5 33558,6 33556,4 population ( N p ), thousand persons Source: Prepared by the author based on information provided by the National Bureau of Statistics (www.statistica.md) (quoted 02/03/2015) *Note: Information presented by the National Bank of Moldova (https://bnm.md/md/medium_ exchange_rates) (quoted 03/02/2015) Therefore, the analysis of the relationship between foreign trade and the national economy can be performed using the following core indicators: Revista Română de Statistică - Supliment nr. 1 / 2016 15 Evolution of statistical indicators on the relationship between foreign trade and economy of Moldova during the years (2003 - 2014) Table 2 Source: Elaborated by the author based on information provided by the National Bureau of Statistics (www.statistica.md) (quoted 05/26/2015) The degree of Moldova’s openness or international ventilation in 2014 was 96.4%, which decreased by 3 p.p. compared to 2013. A value below 100% was recorded both in 2009 (83.9%) and in 2010 (92.8%). For the other years, during the analysed period, the degree of openness recorded values over 100%, reaching the maximum 114.2% in 2007. This share is due to the high values of imports, as the local market is relatively narrow and the domestic base of raw materials and energy resources is insufficient to cover the country’s needs. The share of foreign trade between Moldova and Romania in GDP was 15.6% in 2014, increasing by 1.4 p.p. compared to 2013 and by 6.1 p.p. compared to 2003. The annual advance coefficient in 2014 was 97%; its value below 16 Romanian Statistical Review - Supplement nr. 1 / 2016 100% indicates that the contribution of foreign trade of goods is insufficient for Moldova’s domestic economic processes. Such a contribution is shown in years: 2004 (95.7%), 2006 (97.1%), 2008 (93.8%) 2012 (96.0%), 2013 (98.2%). The remaining years of the period under review show that the dynamic index the of merchandise trade volume exceeds the dynamic index of the nominal GDP in our country. For most years in the period 2003 – 2014 can be stated a sufficient contribution of the merchandise foreign trade of Moldova with Romania, apart from 2009 (representing 66.3%) and 2012 (being 99%). The advance of the dynamics index of country’s foreign trade with Romania to the index of nominal dynamics of GDP growth registered a peak of 124.4% in 2011. During the analysed period (2003 - 2014) the share of Moldova’s trade deficit with all foreign countries in GDP, including Romania, suggests the idea of a passive role of foreign trade in domestic economic processes. The negative contribution of merchandise trade with Romania in the creation of GDP in 2014 was – 4.6%, which, compared to the previous year, increased by 0.7 p.p., due to increased trade deficit with Romanian of 18.7%. The passive maximum contribution of foreign trade between Moldova and Romania was reached in 2006, constituting -5.6%. The coefficient of variation of Moldova’s trade balance in during the years 2004 - 2008 shows that, on average, for every increase unit in GDP, the trade deficit of Moldova with Romania increased by 0.02 units, while in 2010 -2013 the trade deficit with Romania on average for each unit of GDP increased by 0.01 units. In 2014, compared to 2013, for every unit of GDP decrease corresponded an increase of 0,007 units of Moldova’s trade deficit with Romania. Regarding the coverage degree of imports by exports of Moldovan goods with other countries, it can be revealed that during the last 12 years there have been recorded values under 100%. Thus, total merchandise imports in 2014 were covered by exports at a rate of 44.0%, which, compared to the previous year, this coverage decreased slightly by 0.2 percentage points, while compared to 2003 (being 56 3%) fell considerably (12.3 p.p.). Imports of goods from Romania were covered with exports from this country in 2014 at a rate of 54.0%, which, compared to the previous year, decreased by 3 percentage points and 38.3 percentage points compared to 2003, when was registered the highest coverage rate in the analysed period – 92.3%. The index of Moldova’s imports coverage of total merchandise through exports in 2014 was 99.5%, showing a slight negative trend explained by the reduction of imports coverage by exports compared to 2013. This is because, exports decreases more (by 3.7%) compared to the decrease of imports (3.2%). Moldova’s index of imports coverage by exports from Romania was 94.7% Revista Română de Statistică - Supliment nr. 1 / 2016 17 in 2014, being explained, in this case by a stronger import growth (11.2%) compared to export growth of the previous year (5.6%). So the value of imports coverage index with Romania shows that the trade deficit of Moldova with Romania increased in 2014 (being USD 369.1 million) from USD 58.1 million in 2013. Such adverse circumstances – reduction of imports coverage payments generated through revenue earned from exports compared to the base period are emphasised in the years 2005, 2010, 2012, 2013, where the most considerable reduction was recorded in 2004 of 34.7 p.p. (imports coverage index was 65.3%). During the years (2006-2009) and in 2011 imports coverage index with Romania has showed values above 100%, where the highest value of 135.4% was known in 2009 (caused by the financial crisis in America). This year is characterized by a more pronounced pace of decline in imports from Romania (with 47.2%) compared to the pace of decline in exports to the neighbouring country (28.6%), implying significant decrease in trade deficit by USD 182.9 million or about 40%. The intensity of Moldova’s participation in international trade of goods in 2014 amounted USD 657.8 per capita, which compared to the previous year decreased by USD 24.6 per capita (or 3.6%) and compared to 2003 with USD 439.2 per capita (or three times). This indicator, being an indicator of the merchandise foreign trade level of development, represents an expression of performance outside of the national economy, calculated in relation to Romania has experienced an upward trend with little deviation, i.e. recorded decreases for 2009 and 2012 compared to previous year with USD 26.9 per capita, respectively USD 5.5 per capita. Given the fact that exports and imports have different roles in the process of reproduction, these trade flows are reported, usually separately to gross domestic product, forming the following indicators: 18 Romanian Statistical Review - Supplement nr. 1 / 2016 National economy evolution compared to foreign markets, including Romania during the years (2003 - 2014) Table 3 Source: Elaborated by the author based on information provided by the National Bureau of Statistics (www.statistica.md) (quoted 27/03/2015) The share of Moldova’s domestic production for export to foreign countries in 2014 was 29.5%, which, compared to the previous year decreased by 1 per cent, and from 2003 it had a more significant decline of 10.4 p.p. It may be noted that, on average, during the analysed period (2003 - 2014) our country’s total export share was 31.1%. Extension of Moldovan goods to the Romanian market in 2014 was 5.5% of GDP; compared to 2013 it increased slightly by 0.3 p.p. and by 0.9 p.p. compared to 2003. On average, over the studied period, the share of domestic production for export to Romania recorded 4.7%. For each unit decrease in GDP in 2014 compared to 2013 exports of goods made on Romanian market increased by 0.92 units. While in 2013 for every unit increase in GDP compared to 2012, exports of goods in Romania increased by 0.08 units, and in 2012, compared to the previous year the unit growth in GDP corresponds to a decrease of exports of goods made with the neighbouring country. Specific for the year 2009 compared to 2008 is the coefficient of variation of exports to Romania Moldova, which is characterized by the fact that for every unit of GDP decline lays a 0.5 p.p. decrease in exports. The share of total merchandise imports in Moldovan Revista Română de Statistică - Supliment nr. 1 / 2016 19 GDP is much higher than that of exports in GDP, already a known fact. So Moldovan national economy was dependent on international markets in 2014 in the proportion of 66.9% of GDP, where the dependence, compared to 2013, decreased by 2 p.p. When referring to the share of the country’s merchandise imports from Romania, in 2014 this share was 10.1%, which, compared to previous year, increased by 1 p.p. Over the years, since 2003 our country’s dependence on imports of Romanian goods had an increasing trend, registering a share of 4.9% of GDP in 2003, compared to a share of 10.1% from the previous year, with some deviations noted in 2008 and 2009. During these years, compared to the previous year, the share of supplementing domestic resources rate of imports of goods from Romania has decreased by 0.4 p.p. and 4.1 p.p. respectively. Referring to the coefficient of variation in imports from Romania to Moldova in 2014 we can state that every unit of GDP reduction compared to 2013 of imports of goods increased by 3.25 units. From 2003 to 2008 the average growth in each unit of GDP imports from Romanian market increased by 0.13 units in the period (2010-2013) for every unit increase in GDP, which corresponds to an increase of imports on average by 0.8 p.p. Only in 2009, compared to 2008, each unit of GDP reduction registered a decrease in imports of goods from Romania with 0.45 units. In conclusion we can mention that considering Romania as Moldova’s main foreign trade partner in 2014 is a significant event. On top of the partner countries Romania ranks first both in Moldovan exports and imports. Trade relations between Moldova and Romania experienced a good evolution, i.e. an upward trend both in exports and imports of Romanian goods, which was interrupted by significant falls in 2009. This year, under the impact of the global economic crisis decreased the intensity of economic flows between the two countries. References 1. Anghelache C., Anghel M.G. (2015). Statistică. Teorie, concepte, indicatori şi studii ce caz, Editura Artifex, Bucureşti 2. Anghelache, C. (2008). Tratat de statistică teoretică şi economică, Editura Economică, Bucureşti 3. Begu, L.S.. (2009). Statistica internaţională. Bucureşti: Ed. Universitara. 220 p. ISBN 973-743-617-1. 4. Begu, L.S.. (1999). Statistica internaţională. Bucureşti: Ed. ALL BECK. 167 p. ISBN 973-9435-86-6. 5. Bădiță, M., Baron,T., Korka, M. (1998). Statistica pentru afaceri. Bucureşti: Editura Eficient. 591 p. ISBN 973-9366-00-7. 6. Pârțachi, I., Caraivanov, S. (2007). Statistica social-economică. Chișinău: Editura ASEM. 221 p. ISBN 978-9975-75-174-2 7. www.statistica.md 8. www.bnm.md 20 Romanian Statistical Review - Supplement nr. 1 / 2016 Using the Autoregressive Model for the Economic Forecast during the Period 2014- 2018 Prof. Constantin ANGHELACHE PhD. Bucharest University of Economic Studies, Artifex” University of Bucharest Prof. Ioan Constantin DIMA PhD. Valachia University, Targoviste Lect. Mădălina-Gabriela ANGHEL PhD. “Artifex” University of Bucharest Abstract The article is based on the analysis of the autoregressive model. The model will include in its structure a dependent variable represented by the macroeconomic indicator GDP, to be forecasted and as independent variable, granting an autoregressive character to our model, by including in the frame of the built up model of the autoregressive variable GDP (-1), namely the lag 1 of the variable GDP. Also considered as independent variables are the final consumption (FC) and the flow of direct foreign investments (DFI) both influencing the tendency of the evolution of the economic growth in our country. Key words: autoregressive model, indicators, GDP, correlation, forecast, representation Introduction Using the data published by UNCTAD, for the variables included in the frame of the model, the evolutions for the period 2014 -2018 are going to be forecasted. The achieved autoregressive model will include in its structure the macroeconomic indicator GDP, both as dependent variable, to be forecasted and as independent variable, granting an autoregressive character to our model, by including in the frame of the built up model of the autoregressive variable GDP (-1), namely the lag 1 of the variable GDP. Apart the independent variable, GDP(-1), included in the frae of the prediction model, the final consumption (FC) and the flow of direct foreign investments (DFI) are also considered as independent variables which are influencing the tendency of the evolution of the economic growth in our country. We shall develop further on a un model of prediction for the evolution of the GDP, for the period 2014 - 2018, applied on the case of Romania . In order to achieve the prediction, we shall utilize the final data published by UNCTAD during the period 1991 - 2013, for the main macroeconomic Revista Română de Statistică - Supliment nr. 1 / 2016 21 indicators which influence the evolution of the GDP in Romania, respectively: final consumption (FC) and flow of direct foreign investments (DFI), as previously proceeded, in the predictions achieved for identifying the most performing model of prediction. Thus, assuming that the indicators of reference FC and DFI, are going to increase with 10% from one statistical period to another and utilizing in the frame of the first model, which grasps the prediction for the GDP, from the period 2014-2018, the autoregressive variable GDP (-5), we developed, according to the previously followed up stages, an econometric model of prediction, defined as Model 1, (Table 1). The autoregressive model which defines the prima equation noted EQ01 Table 1 Dependent Variable: GDP Method: Least Squares Date: 07/26/15 Time: 18 OS Sample (adjusted): 1996 2013 Included observations: V. ] after adjustments Variable Coefficient Std. Error t-Statistic DFI -0.563367 0.203530 -2.767977 FC 1.274288 0.021404 59.53490 C -4495.684 7S5.7157 -5.721769 GDP(-5) 0.027089 0.016453 1.646514 R-squared 0.999459 Mean dependentvar Adjusted R-squared 0.999343 S.D. dependent var S.E. of regression 1664.960 Akaike info criterion Sum squared resid 3S309273 Schwarz criterion Log likelihood -156.7951 Hannan-Quinn criter. F-statistic 8624.014 Durbin-Watson stat Prob(F-statistic) 0.000000 Prob. 0.0151 0.0000 0.0001 0.1219 104101.1 64969. S9 17.36612 18.06393 17.89340 2.326815 As explained in the previous sub-chapter, where we aimed to test and validate the model of prediction, this time as well we get a valid and correct model, both from the statistical and practical point of view, a fact which is grasped by the outcomes of the tests R-squared and Adjusted R-squared, which underline a percentage of validity of 99.94%, respectively 99.93%. Meantime, the result of these tests is pointing out that the variables utilized in the frame of the model, are exercising a very high influence on the forecasted variable, respectively, the gross domestic product at the level of Romania . We notice that the result evidenced by de F-statistic is also confirming the correctness of the model, the value of 8 624.01, being by far superior to the tableted level, considered as guide mark in the analyses methodology aiming the econometric models. Meanwhile, the result evidenced by Prob (F-statistic), is grasping the degree of null risk of the practical utilization of 22 Romanian Statistical Review - Supplement nr. 1 / 2016 the developed model, Model 1. Thus, the developed, Model 1 can be represented through the equation : GDP = 1.27428*FC - 0.56336*DFI + 0.02708*GDP (-5) - 4495.68 (EQ01) As previously done, we shall have to test whether the proposed model (Model 1), for achieving the prognosis for the evolution of the GDP in Romania is submitting the serial correlation. Further to the application of the test “Breusch-Godfrey Serial Correlation LM Test”, (Table 2), we identified the existence of a serial correlation on the lag 6. We notice this fact through the result obtained after testing the serial correlation, for the series of data utilized in the frame of the built up model, Model 1. Hence, the value of « Prob. Chi-Square (6) », is indicating the acceptance of the null hypothesis according to which : « There is serial correlation on the lag 6 ». The testul Breusch-Godfrey for testing the serial correlation for the Model 1 (EQ01) Table 2 Breuscfi-Godfrey Serial Correlation LM Test F-statistic 5.638039 Prob. F[6,8) Obs’R-squared 14.55735 Prob. Chi-Square(6) Test Equation: Dependent Variable: RESID Method: Least Squares Date: 07/26r15 Time: 18 :05 Sample: 1996 2013 Included observations: 1i Presample missing value lagged residuals set to zero Variable Coefficient Std. Error t-Statistic DFI 0.547795 0.272140 2.380368 FC -0.117421 0.032887 -3.570438 C 2442747 628.5406 3.886379 0.073954 0.024685 2.995896 PIBC-S; RESIDC-1) -1.005891 0.244586 -4.112626 RESIDC-2) -1.426921 0.298148 -4.785940 RESIDf-3) -1.457765 0.293695 -4.963528 RES ID (-4} -1.614681 0.417577 -3.866789 RESID(-5) -1.447887 0.334907 -4.323255 RESID [-6) -1.239866 0.394708 -3.141220 R-squared 0.808742 Mean dependent var Adjusted R-squared 0.593576 S.D. dependent var S.E. of regression 963.2366 Akaike info criterion Sum squared resid 7422598. Schwarz criterion Log likelihood -141.9079 Hannan-Quinn enter. F-statistic 3.758693 Durbin-Watson stat Prob(F-statistic) 0.037847 0.0144 0.0240 Prob. 0.0445 0.0073 0.0046 0.0172 0.0034 0.0014 0.0011 0.0048 0.0025 0.0138 2.75E-11 1510.926 16.87366 17.37331 16.94686 2.OS7003 Thus, in order to improve the proposed model, Model 1, as shown in the Revista Română de Statistică - Supliment nr. 1 / 2016 23 previous sub-chapter, the component AR will be included. The improvement of the proposed model of prediction implies, meantime, the removal of the identified serial correlation. In the achieved graphical representation, Table 3, we showed the future evolution of the GDP, for the proposed prognosis period, respectively 2014 -2018. Out of the statistical tests achieved for the forecasted values, evidenced through the equation EQ01, of the developed model, Model 1, we observe that the value “RootMean SquaredError” is indicating a very small prediction error, counting for 601.28. The representation of the prediction for the GDP (Model 1), during the period 2014 -2018, in Romania Table 3 Following the stages proposed in the previous sub-chapter, for the process of validating the prediction model, taking into consideration the increase of the accuracy level of the estimation of the evolution of the GDP, achieved through the equation EQ01, resulting out of Model 1, we shall remove the serial correlation, by including the component AR(1), which would ameliorate the quality of the dynamic prediction model. Further on, we shall develop an autoregressive model, containing the AR(5), called Model 2, which, according to the Eviews outcomes may be represented mathematically as follows: GDP = 1.27137*FC - 0.50020*DFI + 0.03072*GDP (-5) - 0.09860*AR(-5) - 4854.68 24 Romanian Statistical Review - Supplement nr. 1 / 2016 Similar to the situation of the models previously interpreted, a very high level of accuracy of the built up prediction model - (Table 4) – is to be observed. This aspect is underlined in a proportion of 99.91%, respectively 99.87%, by the outcomes of the tests R-squared, respectively Adjusted R-squared. Meantime, the value Prob(F-statistic) as well, is underlining a degree of null risk in the situation of the implementation of the built up model, Model 2, in practice, in the predictions for the evolution of the gross domestic product at the level of our country``. Meantime, the achieved model gets a high applicability given by the individual values of the probabilities obtained by each variable alone, (Table 4), which evidences the fact that it is significant for explaining the evolution of the gross domestic product in Romania. Model 2.The autoregressive model which defines the second equation EQ02 Table 4 As previously specified, the component AR(5) is improving the proposed model of prediction, eliminating meantime the serial correlation. Thus, applying the test “Breusch-Godfrey Serial Correlation LM Test”, in order Revista Română de Statistică - Supliment nr. 1 / 2016 25 to verify whether the serial correlation is still identified on the lag 6, through processing in Eviews 7.2, we get the outcomes pointed out in the Table 5. According to the probability result, “Prob Chi-Square (6)”, obtained for the assumed null hypothesis, according to which “« There it would exist a serial correlation on the lag 6 », for the series utilized for the construction of the model, Model 2, we deduce that this one does not submit a serial correlation any more. Thus, we observe that the Model 2, which comprise the component AR on the lag 5, is submitting an accuracy very little improved as against the Model 3, a fact underlined by the result obtained for the test “RootMean SquaredError”, which indicates an estimated error between the possible real values of the evolution of the GDP and those estimated by the prediction model, counting for 599.10, Table 6. The test Breusch-Godfrey for testing the serial correlation for the Model 2 (EQ02) Table 5 26 Romanian Statistical Review - Supplement nr. 1 / 2016 We remained that the achieved prediction was grounded on the presumption that the considered macroeconomic indicators in the construction of the prediction model (final consumption, respectively flow of direct foreign investments), would increase by 10%, from one year to another. Thus, in conformity with the previous statements, the graphical representation of the forecast achieved with the help of the informatics softs Eviews 7.2., from the point of view of the Model 4, previously built up, is of the form (Table 6): The representation of the GDP forecast (Model 2), during the period 2014 -2018, in Romania Table 6 In this context, according to the values submitted in the tables below (Table 7), the forecasted evolution of the gross domestic product in Romania, for the period 2014 -2018, is grasped in parallel by the equations: EQ1PIBF (representing the first equation of prediction for the GDP) and EQ2PIBF (representing the second equation, anticipating the evolution of the GDP for the considered prediction interval). As shown by the tests « Root Mean Squared Error » as well, achieved for both Model 1, and Model 2, (Table 3 – Table 6) there are no major differences between the comparative evolutions of the values recorded by the GDP, for the 2014 -2018, in both cases considering the assumed supposition concerning the increase of the consumption and the drawn foreign capitals with 10% in each forecasted year, Table 7. Revista Română de Statistică - Supliment nr. 1 / 2016 27 The parallel representation of the outcomes of the expost predictions grasped by EQ01 (Model 3) vs EQ02 (Model 2) for the period 2014 -2018 Table 7 Obs 1991 1992 1993 1994 1995 1996 1997 199S 1999 2000 2001 2002 2003 2004 2005 2006 2007 2003 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 GDP 30593.07 2075975 27951.94 31887.80 37618.60 36911.15 35616.63 41749.52 35995.56 37305.10 405S5.S9 45988.51 59466.02 75794.73 99172.61 122695.85 170616.95 204338.60 164344.38 164792.25 182610.67 169396.06 186438.48 205082.33 225590.57 248149.62 272964.59 300261.04 EQ1_PIBF NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 204402.6 224859.3 247331.1 272211.1 299384.6 EQ2_PIBF NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 204367.5 224984.3 247686.7 272190.4 299941.1 Hence, if the macroeconomic indicators: final consumption, respectively flow of direct foreign investments increase with 10% from one year to another, over the interval 2014 -2018, according to the built up dynamic prediction models, the GDP would record the value of 204 367.5 million USD, in the year 2014, reaching the level of 299 941 million USD in 2018, values estimated through the Model 2 (EQ2) of prediction, which contains the component AR(5). If we consider the equation EQ01, evidenced by the Model 1, the evolution would not differ too much, the GDP of our country going to reach level of 204 402.6 million USD, in the year 2014, so that in 2018 it records the value of 299 884 million USD. Out of the graphical representation submitting the dynamic prediction of the GDP evolution (Table 8), during the period 2014 -2018, we notice the very close values estimated by the two built up models (Model 1 and Model 2). Thus, the red dotted line of the graphical representation, respectively in the Table 8, achieved with the help of the informatics soft Eviews 7.2., is showing the prediction estimated by utilizing the equation EQ 1, namely EQ1PIBF, established as a result of the 28 Romanian Statistical Review - Supplement nr. 1 / 2016 development of the dynamic model, Model 1, while the green line emphasizes the estimated prediction through the equation EQ02, respectively EQ2PIBF, established through the econometric model defined as Model 2. The representation of the GDP forecast, through the two models (Model 1 and Model 2) represented by EQ01 vs EQ02 during the period 2014 -2018, in Romania Table 8 Conclusions The test “Breusch-Godfrey Serial Correlation LM Test”, nidentifies the existence of a serial correlation on the lag 6. We notice this fact through the result obtained after testing the serial correlation, for the series of data utilized in the frame of the built up model, Model 1. The value of « Prob. ChiSquare (6) », is indicating the acceptance of the null hypothesis according to which : « There is serial correlation on the lag 6 ». The future evolution of the GDP, for the proposed prognosis period, respectively 2014 -2018. Out of the statistical tests achieved for the forecasted values, evidenced through the equation EQ01, of the developed model, Model 1, we observe that the value “RootMean SquaredError” is indicating a very small prediction error, counting for 601.28. The component AR(5) is improving the proposed model of prediction, eliminating meantime the serial correlation. Thus, applying the test “BreuschGodfrey Serial Correlation LM Test”, in order to verify whether the serial Revista Română de Statistică - Supliment nr. 1 / 2016 29 correlation is still identified on the lag 6, through processing in Eviews 7.2, we get the outcomes pointed out in the Table 5. The Model 2, which comprise the component AR on the lag 5, is submitting an accuracy very little improved as against the Model 3, a fact underlined by the result obtained for the test “RootMean SquaredError”, which indicates an estimated error between the possible real values of the evolution of the GDP and those estimated by the prediction model, counting for 599.10, Table 6. According to the values submitted in the tables below (Table 7), the forecasted evolution of the gross domestic product in Romania, for the period 2014 -2018, is grasped in parallel by the equations: EQ1PIBF (representing the first equation of prediction for the GDP) and EQ2PIBF (representing the second equation, anticipating the evolution of the GDP for the considered prediction interval). As shown by the tests « Root Mean Squared Error » as well, achieved for both Model 1, and Model 2, (Table 3 – Table 6) there are no major differences between the comparative evolutions of the values recorded by the GDP, for the 2014 -2018, in both cases considering the assumed supposition concerning the increase of the consumption and the drawn foreign capitals with 10% in each forecasted year, Table 7. References 1. Andrei, T.; Stancu, S.; Iacob, A.I. (2008). Introducere în econometrie utilizând EViews, Editura Economică, Bucureşti 2. Anghel M.G. (2013). Modele de gestiune şi analiză a portofoliilor, Editura Economică, Bucureşti 3. Anghel M.G. (2011). Analiza evoluţiei unui activ financiar pe baza modelului Auto Regressive Conditional Heteroskedasticity, Revista Română de Statistică, Supliment Trim III 4. Anghelache C., Manole A., Anghel M.G. (2015). Analysis of final consumption and gross investment influence on GDP – multiple linear regression model, Theoretical and Applied Economics, No. 3/2015 (604), Autumn 5. Anghelache C., Anghel M.G. (2014). Modelare economică. Concepte, teorie şi studii de caz, Editura Economică, Bucureşti 6. Anghelache C., Anghel M.G. et al. (2013). Using Time-Series Analysis of Economic and Financial Phenomenon, Revista Română de Statistică – Supliment/Trim III 7. Anghelache, C.; Anghelache, G. (2009). Utilization of the chronological series within the Stochastic processes, Metalurgia Internaţional Vol. XIV, nr. 4, Editura ştiinţifică F.M.R 8. Boshnakov, G.N.; Iqelan, B.M. (2009). Generation of Time Series with Given Spectral Properties, Journal of Time Series Analysis, Vol. 30, Issue 3, pp. 349368 9. Cavaliere, G.; Rahbek, A.; Taylor, R. (2008). Testing for Co-Integration in Vector Autoregressions with Non-Stationary Volatility, CREATES Research Paper No 2008-50, Univ. of Copenhagen of Economics Paper No. 08-34 30 Romanian Statistical Review - Supplement nr. 1 / 2016 10. Franco, Ch.; Zakoian, J.M. (2004). Maximum Likelihood Estimation Of Pure GARCH And ARMA-GARCH Processes, Bernoulli 10(4), 605–637 11. Gujarati, D. (2004). Basic Econometrics– Fourth Edition, The McGraw – Hill Companies 12. Snowberg, E.; Wolfers, J., Zitzewitz, E. (2012). Prediction markets for economic forecasting, Working Paper 18222 Revista Română de Statistică - Supliment nr. 1 / 2016 31 Managing Financial Instruments by Development Bank of Romania Prof. Vergil VOINEAGU PhD. Bucharest University of Economic Studies Prof. Michal BALOG, PhD. Technical University of Košice Daniel DUMITRESCU PhD. Student Diana SOARE (DUMITRESCU) PhD Student Bucharest University of Economic Studies Abstract Eximbank direct empowerment group analysis as administrator of financial instruments provided by the Managing Authorities under the Operational Programmes of Romania 2014 - 2020 is one of the options provided by the EU Regulation 1303 of the current 2013 financial data the Export - Import Bank of Romania as well and solvency indicators (situated at a high level) accounted together for portfolio diversification of financial products designed to assist national economy, important factors in designating this institution as a financial intermediary of European funds that will be allocated as financial instruments. Keywords: financial intruments, funds, guarantees, loans, funding, profitability, development bank, financial institution, authority Introduction Based on the Government Programme into force in 2015, the executive Romania approved in March 2015, documents relating to the National Bank for Development, which established the enlargement of the mandate of the current banks of export - import, Eximbank to achieve benches Development and to update the legislative framework to fit these new activities (Law 96/2000, republished, Government Decision No. 534/2007 respectively). The mandate given by the Romanian Government Romanian Development Bank - Eximbank SA, aims to achieve a number of goals, such as funding priority areas, supporting export Romanian and Romanian investments in foreign, all contributing to the implementation of EU policy objectives in local and alignment with European and international practices in the field of financing for development and the absorption of EU funds. This institution is to remedy market failures private financing of SMEs and other actors in 32 Romanian Statistical Review - Supplement nr. 1 / 2016 Romania. Development Bank will provide various sources of funding to develop and implement financial products specific to different categories of eligible beneficiaries. To operate within the law, the Government proposed to repeal the current law Eximbank (Law 96-2000) in order to regulate a modern and efficient manner, based on European best practices, work in behalf of the state of this new institution financial - banking. Methodology and data European Commission by Eurostat shows relevant statistical data on key aspects of business and European funds in Romania and other EU countries. Multiannual Financial Framework 2014 – 2020 It offers three options for implementation by Member States of financial engineering instruments and the choice of the implementing bodies are financial intermediaries: a. financial instruments set up at Union level and managed by the European Commission directly or indirectly, such as for example: COSME and Horizon 2020, for which the European Investment Fund and the European Investment Bank received direct mandate from the European Commission ; b. Financial instruments established at national or regional administration in accordance with common rules, which can be existing financial instruments or financial instruments standardized; c. loans or guarantees to financial instruments implemented directly by managing authorities. Compared to the above, and given the financial market analysis in Romania during 2007 - 2013 for the Multiannual Financial Framework 2014 - 2020 looming many variations of European financial instruments, and more choice of institutions to manage. Thus, in December 2015, when we mark the end of the use of structural funds and cohesion related BFM 2007-2013 (under rule n + 2), the principle of complementarity seems to work best for Romania, opts for the formula optimal management of EU financial instruments to be applied in all 3 versions provided by the European regulations. In the period 2012-2013, Eximbank has seen an acceleration of activity, performance reaching than those of other commercial banks in Romania. In recent years the Bank managed to obtain operating profit, which in the current financial performance is even more noteworthy since the case of an institution with public capital. Only in 2014, Eximbank has reached an exposure of five billion lei. Data released by the institution that since 2008, the bank’s exposure to an increase continues to the present: Revista Română de Statistică - Supliment nr. 1 / 2016 33 Source: EximBank, Financial Report 2014, interpretation Author On the other hand, the funding is defaulted to a level of 3.1%, well below the average of other credit institutions in the market. Compared with existing results in the financial markets - banking in Romania, Eximbank enjoy better profitability, which are based on a number of generally low-risk assets. Also, as can be seen below, the indicators of liquidity and solvency are upper limits. The indicator on the credit institution’s capital adequacy, calculated according to the statute in force in December 2014, has a robust solvency ratio amounts to 60%. Source: EximBank, interpretation Author Involvement of EXIMBANK in matters regarding European funds In terms of EU funds absorption Eximbank them engaged in developing and promoting a package of financial products necessary for the proper implementation of a European project. As specific product cycle management of European projects, the institution proposes comfort letters, pre-financing, co-financing, insurance and other types of collateral. Source: EximBank, interpretation Author 34 Romanian Statistical Review - Supplement nr. 1 / 2016 Contribution to European funds absorption was successful by Eximbank financing and guarantee through specific products. Thus, for 2014, the institution has mobilized amount of 103 million lei worth in various fields as you can see above. a. Letters of Comfort issued by Eximbank are targeting both SMEs and large enterprises or other legal entities of public law which submitted projects that have been declared the winner by the management authority, will sign the financing contract. b. Pre-financing loan provides the necessary capital for the period between payment of invoices by the customer and payment of claims for reimbursement made by management authorities. The maximum loan period is 2 years and the funding can be given in euro or lei. This type of loan can cover up to 100% of funding from AM Repayment of loans is based on the timing of receipts from the management authority. c. Loans to finance European projects can cover all project costs (eligible and ineligible) and working capital. Duration of funding depends on the period of project implementation, can be short, medium and long term, and currency lei or euro can be granted. During project implementation, Eximbank may grant a grace period. The amount of funding is capped at 85% of the beneficiary’s own contribution. After analyzing several European business models, among which we note HBOR in Croatia, KfW of Germany, BDB in Bulgaria, it was agreed that the best suited to our objectives of economic development as BGK model, Gospodarstwa Krajowego Bank of Poland. Considering the performance that Poland obtained as EU member state absorption of structural funds and Couze European 2007 - 2013, it is no wonder that temeatica European funds will be extremely important for the future Romanian Development Bank Eximbank . In this respect it is estimated that the Multiannual Financial Framework 2014 - 2020, the institution will provide co-financing of European projects representing commitments of our country to the European Union with an estimated impact of Euro 6 billion. Provisions of the law project To clearly define the mandate of the Romanian Development Bank granted by the Romanian state and to provide an appropriate framework for its specific activities, the bill establishes a clear distinction between commercial banking entirely subject to regulatory requirements relevant level local and Revista Română de Statistică - Supliment nr. 1 / 2016 35 European levels of development and activity of banks operating as state representative, affirming the principle of complementarity between the two activities, and support the development Bank of strategic economic sectors in accordance with national government policies. Source: EximBank, interpretation Author Meanwhile, the bill defines the categories of beneficiaries eligible for various specific banking products development banks, focusing on those types of clients deemed to have a risk profile higher, but with the potential for creating added value and the creation of jobs. These categories of beneficiaries may include, but are not limited to start-ups (start-up), small and medium enterprises, and associations and foundations (NGOs), intra-community development associations, LAGs or FLAG- Links to retain private financial market in 2015 a certain reserve and distance. Development Bank will give the latter a wide range of financial products - bank most diverse but complementary and necessary to run any investment project, whether financed from European funds or credits, cofinancing, refinancing, guarantees of loans , insurance policies or reinsurance. Source: EximBank, interpretation Author These products require significant financial resources, the legislature proposing various sources of funding for the institution. The Government will 36 Romanian Statistical Review - Supplement nr. 1 / 2016 provide resources from Ministry of Finance, revenues that will come from privatizations of state enterprises, or financial resources to be granted by mandate from the various public authorities, or the Development Bank may use external financing lines from international financial institutions and other development banks. Conclusions In this context, the Romanian Bank for Development will receive management project to promote small and medium enterprises, stages I, II, III project involves the administration of the financial revolving granted by the German Government, by Kreditanstald fur Wiederaufbau (KfW) to the Ministry of Finance during 1998-2008. Although the credit institution will state capital, however, will opt to supplement funding from the state budget and by securing sources of additional financing, so as to ensure the soundness JEL institution and to reach in an efficient way lending targets assumed by law. These additional sources of funding will enable expansion specific made in behalf of the state and to other related activities development banks. References 1. Anghelache, C., Anghel, M.G. (2015). Theoretical aspects concerning the use of the statistical-econometric instruments the analysis of the financial assets, Romanian Statistical Review - Supplement, No. 9 2. Anghelache, C., Anghel, M.G. (2014). Using the Regression Model in the Analysis of Financial Instruments Portfolios, Procedia Economics and Finance, Volume 10 3. Anghelache, C., Anghel, M.G. (2014). Economic modeling: Concepts, theories and case studies, Economica Publishing House, Bucharest 4. Droj, L. (2014). „Bancabilitatea proiectelor de investitii finantate din Fonduri Structurale Europene”, Editura Economica, Bucharest 5. Dumitrescu, D.I., Soare, D.V. (2014) – „ Financial Engineering Instruments Financed from European Structural and Investment Funds and Financial Products issued by Financial Institutions supporting European Project Implementation”, Romanian Statistical Review Supplement, Volume (Year): 62 (2014), Issue (Month): 10 (Octomber), pp. 16-39 6. Păunică, M. (2014) - Economic benefits of the infrastructure projects implemented in the Reservation of the Danube Delta Biosphere, Theoretical and Applied Economics, Volume (Year): XXI (2014), Issue (Month): 11(600) (November), Pages: 95-104 7. Vaida-Muntean, C., Voineagu, V., Munteanu, G. (2014) – „Uncertainty And Sensitivity in Statistical Data,” Romanian Statistical Review Supplement, Volume (Year): 62 (2014), Issue (Month): 12 (December), pp. 29-36 8. http://ec.europa.eu/eurostat/web/structural-business-statistics 9. www.economica.net 10. mfinante.ro Revista Română de Statistică - Supliment nr. 1 / 2016 37 Essential aspects regarding the optimal prevention Prof. Constantin ANGHELACHE PhD. Bucharest University of Economic Studies Prof. Mario G.R. PAGLIACCI PhD. Universita degli Studi di Perugia Emilia STANCIU PhD. Student Cristina SACALĂ PhD. Student Bucharest University of Economic Studies Abstract It is recurrently very likely for decision-makers to wrongly assess risk and consequently to ineffectively put effort in diminishing or avoiding it. Preventing losses or self-protection represent an effort we make in order to reduce the impact of a probable accident. The question is: what would be the level that maximizes the estimated utility of an economic agent? In many cases, the cost-benefit an analysis of prevention is analyzed based on the risk-neutrality assumption. This means that only losses of average size matter. There is no intention of reducing the variability of losses if it doesn’t involve a loss above average. The extreme measure of risk prevention is giving up any type of risk. Key words: prevention, assets, risk, aversion, optimization Introduction Optimality of activities of prevention against loss was initially examined by Ehrlich and Becker (1972). They called this type of activity “self-protection” and proved that insurance and prevention can be either complementary or substitute each other. Effects of potential risks in activities of risk prevention were examined by Briys, Schlesinger si Shulenburg (1991). Dionne si Eeckhoudt (1995) demonstrated that an increase in risk aversion has an ambiguous effect because prevention does not generate a risk reduction as Rothschild si Stiglitz stated. Julien, Salanie si Salanie (1999) showed that an increase in risk aversion led to an increase in optimal level of effort if and only if the initial optimal probability of loss is smaller than the dependent threshold of utility. Chiu (2000) showed for the first time that the third derivative of utility function had an important part in determining the threshold. Jewitt (1989) and Athey (2002) examined the effect of risk aversion on optimal decision for general issues, including prevention as a special case. 38 Romanian Statistical Review - Supplement nr. 1 / 2016 2. Aspects regarding prevention in case of risk neutrality Let us consider the case in which a risk-neutral agent may have the risk of losing a quantity L with probability p. The agent may invest in preventive measures to reduce the loss probability. If e is the amount of money invested in prevention, the probability of loss L is p(e). Let us assume that P p is a twice differentiable, decreasing and convex function p’ ˂ 0 and p’’ ≥ 0.The convexity condition is that the prevention activity should have a decreasing marginal productivity. The risk-taker should select e so as to minimize the expected net cost of risk, taking into account the cost of prevention. This can be written: (1) Since C is convex in e, the first order condition is necessary and sufficient for a minimum. In the end, we assume that C’ (0) ˂0, so as the constraint e≥0 is not binding. The preventive optimal investment en for a risk neutral agent is defined by: -pˈ(en)L=1 (2) This equation is the classical optimization condition according to which the marginal cost is equal to marginal benefit. This gives a positive probability of loss, since a total elimination of risk is usually extremely expensive. It is possible to obtain a situation with risk 0 only from the technical point of view, but not from the economic point of view. Risk aversion and optimal prevention The hypothesis of risk neutrality is of good approximation when risk is low or can be diversified on the market. Let us consider an estimated utility maximize with an welfare degreew0who risks to lose quantity L cu with a probability p(e). The decision can be written: (3) With probability p(e),final wealth isw0 - e – L otherwise isw0 – e. When u is linear, programs (1) and (3) are equivalent, since V(e) = a-bC(e) for (a,b ˃ 0). Comparing optimal prevention e*of estimated utility maximizer withe*, the optimal prevention of risk neutral decision maker, we observe that risk-aversion agents invest more in risk-prevention and risk takers invest less. Revista Română de Statistică - Supliment nr. 1 / 2016 39 Condition type 2 is not necessarily fulfilled even in the case of riskaversion. We have: Where the first term is negative, the second and the third term is positive, respectively negative, in case of risk aversion. Consequently, we can not take for a certain fact that V is concave without establishing supplementary restrictions to u and p.Assessing this derivative, we come to the conclusion that: where Using the condition (2) we observe thatV’(en) only if where z= w0 - en The equation can be written: The right part of this inequality is positive under risk aversion. Risk aversion increases optimal investment in prevention if and only if the loss probability which is optimal for risk neutral agent is smaller than a critical threshold where: Risk aversion does not necessarily increase the optimal investment in prevention. More prevention would lead to a reduction in wealth in both cases and risk aversion agents would not like the decreasing welfare. The critical point is½ if the utility function is quadratic, for instance if prudence degree is 0.The quadratic agent measures risk by its variant with a maximum at p=1/2 40 Romanian Statistical Review - Supplement nr. 1 / 2016 If pn is smaller than ½, an increase in loss prevention reduces p and which is desirable by risk- aversion quadratic agents. If pn is bigger than ½, an increase in loss prevention reduces p but increases σ2. σ2and, Aspects regarding prudence and optimal prevention A generally accepted theory is that prudent people invest more in prevention. Yet, this is contradicted by practice. In order to isolate the effect of prudence (u’’ ˃0) or imprudence (u’’’˂0), we limit the analysis to the case in which risk-neutral agents select pn=1/2. Risk aversion does not influence optimal investment in prevention for quadratic utility, since quadratic preferences show prudence 0, uˈˈˈ=0. In such a situation, a prudent agent would invest more in prevention than a risk neutral agent? More prevention is optimal if condition (5) is fulfilled, for instance (6) We assume that agent is imprudent. (u’’’˂0).Using Jensen’s inequality for each possible value of the integrand below, we get If prudence (u’’’›0), we obtain the opposite result. So, in case we assume that a risk-neutral agent takes he effort en so as the loss probability should be ½, all prudent agents will choose an effort smaller pn while all imprudent agents choose an effort bigger than en. Prudence increases the marginal value of welfare so it reduces the willingness to invest in financing prudence. So, a prudent agent will save more cautiously as a protection against loss than an imprudent agent. Conclusions Risk approach is important when analyzing financial decision set. Any decision is based on certain evaluations of the probability of risk emergence. Especially financial decisions need a particular framing given the efforts of altering the decisional circumstances and the nature of risk itself. Investments for risk reducing alter risk distribution as opposed to insurance, which finally alter risks’ consequences financing, normally accepted as loss control. The way the distribution is altered represents a rather complex process. Loss prevention is a certain type of loss control, known also as “selfRevista Română de Statistică - Supliment nr. 1 / 2016 41 prevention”, which is the amount of effort made with the view to reduce the probability of an unwanted event. References 1. Anghelache C., Anghel M.G., Sacală C. (2015). Optimum analysis model in the case of deductible insurance, Romanian Statistical Review - Supplement, No. 11, pg. 9 – 13 2. Anghelache C., Anghel M.G., Niţă O.G., Ursache A. (2015). Unele aspecte privind modelul de coasigurare optimă, ART ECO - Review of Economic Studies and Research, Vol. 6/No. 3, pg. 40-47 3. Anghelache C., Anghel M.G., Popovici M., Dumitrescu D. (2015). Utilizarea modelelor econometrice în analiza comparativă în domeniul coasigurării, ART ECO - Review of Economic Studies and Research, Vol. 6/No. 3, pg. 75-80 4. Anghelache C. (2011). Analiză actuarială în asigurări. Note de curs, Editura Artifex, Bucureşti 5. Anghelache C., Mitruţ C., Pârţachi I. şi alţii (2009). Cadrul legal al calculului primei de asigurare”, Revista Română de Statistică - Supliment, nr. 7, pp. 143145 6. Athey S. (2002). Monotone comparative statics under uncertainty, Quarterly Journal of Economics 7. Briys, E., H. Schlesinger, and J.-M. Schulenburg (1990). Reliability of risk management: market insurance, self-insurance and self-protection reconsidered, Geneva Papers on Risk and Insurance Theory 8. Courbage, C., and B. Rey (2007). Precautionary saving in the presence of other risks, Economic Theory, 32(2), 417–424. (2012): Optimal prevention and other risks in a two-period model, Mathematical Social Sciences, 63(3), 213–217 9. Courbage, C., B. Rey, and N. Treich (2013). Prevention and precaution, in Handbook of Insurance, ed. by G. Dionne, Huebner International Series on Risk, Insurance and Economic Security, chap. 8. Springer. 10. Dachraoui, K., G. Dionne, L. Eeckhoudt, and P. Godfroid (2004). Comparative mixed risk aversion: Definition and application to self-protection and willingness to pay, Journal of Risk and Uncertainty, 29(3), 261–276 11. Dionne, G.and L. Eeckhoudt (1985). Self-insurance, self-protection and increased risk aversion, Economic Letters 12. Eeckhoudt, L., R. Huang, and L. Tzeng (2012). Precautionary effort: A new look, Journal of Risk and Insurance, 79(2), 585–590 13. Jewitt, I. (1989). Choosing between risky prospects: the characterization of comparative statistics results, and location independent risk, Management Science 14. Lee, K. (2012). Background risk and self-protection, Economics Letters, 114(3), 262– 264 42 Romanian Statistical Review - Supplement nr. 1 / 2016 Specific elements of correlation between infomation and risk Assoc. prof. Alexandru MANOLE PhD. “Artifex” University of Bucharest Emilia STANCIU PhD. Student Alexandru URSACHE PhD. Student Bucharest University of Economic Studies Abstract The aspects regarding the correlation between information and risk have been object of scientific research for a log time. Many scientists have analysed it both from the qualitative and quantitative point of view. This article aims to present a general model of using information in risk prevention and emphasize the value of information for risk-aversion decision makers. Introduction Although progress in financial decisions has been noticeable in the latest decades, there are still unsolved or partially-solved issues mainly due to the complexity of this field Any type of financial decision has two opposite, yet inseparable aspects: objective information and subjective opinion on it. As a consequence, the expected utility of information greatly depends on the decision- maker’s particularities. The more society evolves, the bigger is the quantity of information, the more aware are people of their subjective limitations that influence their decisions, the less is time for decision-taking. Therefore things are getting more and more complicated, not simpler. Risk is always sensitive to new information. For instance, financial news we find out every day influence our perception on the risk level of the investments we have to make. It depends only on us whether we take into account the news and to what extent they influence our decisions. Notions regarding the role and value of information The more accurate and available is the information, the more probable is for us to take the right decision. For instance, if we consider the case of an insurance. A person named X, with an intial welfare of 4000 ron has to transport it over the ocean. If evreything all right, the welfare will increase to 8000 Revista Română de Statistică - Supliment nr. 1 / 2016 43 ron. As X is a risk-aversion person, he takes into consideration the posibility of making an insurance. An insurance company offers him only one type of contract accordind to which, if the ship sinks, X gets 8000 ron. The prize for a full insurance contract is 4400 ron. If the probablity of loss is ½, this prize has a corresponding recovering factor of 0,1. X utility function is If X takes the full insurance contract, the expected utility function is: Due to the full insurance, the expected utility is independent from X’s opinion about the probability of an accident. Let us consider P= p0 the subjective probability of X in absence of any information. /in this case, we let p0 unspecified. If X decides not to make any insurance, the expected utility is: His decision can be written: where V0 is maximum utility of X in absence of any information. Therefore, if X has no information, the optimal startegy is to let the ship with no insurance. Let us now consider the case that X can obtain free information before making any insurance and the insurance company does not know that the client knows this information. X can receive good or bad signals of his success, with q şi respectiv 1-q probabilities. He calculates the posterior probability of success by using Bayes rule. Before observing the sgnal, the probability of success is as before. So, I shall assume that po=0.5, q=0.5. V0 is the maximum expected utility when the probability of success is p0 Fig. 1 44 Romanian Statistical Review - Supplement nr. 1 / 2016 Expected maximum utility Vi with information Fig. 2 The curved line V(p) is the maximum expected utility as function of probability p of success. Information is relevant for the probability of success. As seen in Fig. 1, the maximum expected utility is a convex function of probability of success p, information leading to an increase of welfare: Fig. 2. Shows us how this happens. The investor has the option of making or not the insurance in case he receives a good signal. He may insure against risk regardless the signal. Nevertheless, information is useful and valuable if the person who knows them takes the consequent decisions in order to prevent or diminish the possible risk. A general model regarding the use of information in deceasing risk A piece of information may be non-negative if the decision taking process does not depend on it. An informed decision-taker may behave at least like a not informed decision-taker if he decides to disregard the information he has. It is worth mentioning that the linearity of expected utility by respecting the probabilities is an essential element. The value of information and risk- aversion It is commonly believed that decision-takers with a greater riskaversion give information more importance. Yet, this is not a general rule. Revista Română de Statistică - Supliment nr. 1 / 2016 45 Let us consider that wb şi wg represent safe levels of the risk-taker’s welfare when he receives signals pb and pg. We also consider that w0 şi wi are equivalents of certitude in absence or presence of information. Without any kind of information, the decision- maker does not take any kid of risk w0=7600 ron. If he receives a bad signal, he will make an insurance, in which case wb=w0. The only interesting case is when pg is bigger than pc , case in which a good signal determines X to make an insurance. We can extend the previous formula to get: We have a case where information determines the decision-maker to take a risk he normally won’t take in case of lack of information. In the picture below, the value of information k is represented as a function of the degree of relative aversion to risk y. A full insurance is optimal if the relative risk aversion is bigger than ƴ =2.390. An ex-ante exposal to risk is bigger when we lack the information, since an insurance is still optimal when a bad signal is noticed. Simetrically, a bad signal determines the decision-taker to insure against a risk he wouldn’t normally have insured if he hadn’t had the information. Aspects regarding the effect of information on behaviour I shall consider the following model with two periods: At the moment 0, the decision taker selects α0, which means a good state u(w0) for the first period. Then, at the beginning of the next period, he notices a signal m which influences his opinions s̃. He chooses to maximize the expected value. The decision in the first period influences the second period in two ways. Firstly, the set of choises α in the second period may be greatly 46 Romanian Statistical Review - Supplement nr. 1 / 2016 influenced by initial choice of α0 . Secondly, the initial choice of α0 at time 0 may directly influence utility in the second period. The decision taker is in a situation of probabilistic incertitude in the first period. He is confronted with the parameter risk: the incertitude related to the distribution parameters for the risk he has to take. The time needed for this type of decision is crucial as incertitude increases in time. In this type of decision-taking process we have to consider the ireversibility conditions. If the decision-taker regrets the initial decision and is not flexible along all along the decision-taking process, the risk management is in danger. The decision of optimal saving is another kind of decision to be considered. Better information gives the risk-taker the chance to diversify the future risk. Prudent risk-takers will make prudent savings. Hirshleifer effect is one of the effects that appear in the risk-taking process. Let us assume that all risk-aversion makers confront with a risk of damage x̃. Likewise, we’ll assume that we have a competitive market with no cost of transaction (ƛ=0) and no asinometric information. For decision-makers an equilibrium state would be a total insurance against risk at a minimum premium price Ex̃. The cst of information is equal to the premium of risk associated with x̃ since the decision-makers would rather take risk x̃ than the middle of E x̃ . This is Hirshleifer effect. Conclusions In an ideal economy, decisons are made based on rationality and optimality. Decision-takers are considered to act only after they have taken into account all available information. However, recent research has proved that the ability of gathering direct infrmation, the time we vae at our disposal, irationality, biases, personal features play an important role in the risk-taking process. It is widely accepted that a decision is better if the available information is in a bigger quantity and of a better quality. But, from this point on, a key element is the decison-maker’s ability of evaluating the information in complex circumstances. Using probabilistic and deterministic models is an artificial way to correlate information in order to facilitate the process of takig better decisions. A bad decision is not due to the very use of a certain model, but to the wrong or abusive use of it. References 1. Anghel, M.G., Ursache Alexandru (2015). Elemente definitorii privind riscul economic, ART ECO - Review of Economic Studies and Research 2. Anghel, M.G. (2013). Modele de gestiune şi analiză a portofoliilor, Editura Economică, Bucureşti 3. Anghel, M.G. (2010). Utilizarea modelelor econometrice în analizele economice, Simpozionul Ştiinţific Internaţional „Necesitatea reformei economico – sociale a României în contextul crizei globale”, Editura Artifex, Bucureşti, pg. 145-151 4. Anghel M.G., Pocan O. (2009). Modele de estimare a rentabilităţii şi riscului unui Revista Română de Statistică - Supliment nr. 1 / 2016 47 titlu financiar, Revista Română de Statistică – Supliment „Criza economică - efect, cauze, perspective”, nr. 3, pg. 337 – 340 5. Anghelache C., Manole A., Anghel, M.G., Stanciu Emilia, Soare D.V.(2015). Aspecte semnificative privind analiza riscului de faliment, ART ECO - Review of Economic Studies and Research, Vol. 6/No. 3, pg. 15-21 6. Anghelache, C., Anghel, M.G. (2015). Aspectele principale referitoare la unele modele non-liniare utilizate în analizele economice /Main Aspects Regarding Some Non-Linear Models Used in Economic Analysis/ Revista Romană de Statistică Supliment/Romanian Statistical Review Supplement, 9/2015: 3-11 7. Anghelache C., Manole Alexandru, Anghel, M.G., Popovici M., Soare D.V. (2015). Modele de analiză a riscului finanicar ART ECO - Review of Economic Studies and Research, Vol. 6/No. 3, pg. 55-63 8. Anghelache, C. (2008). Tratat de statistică teoretică și economică, Editura Economică, București 9. Anghelache, C., Capanu, I. (2003). Indicatori macroeconomici – calcul și analiză economică, Editura Economică, București 10. Anghelache, C. (coord.) (2014). Statistical-econometric models used to study the macroeconomic correlations, Romanian Statistical Review-Supplement, December 2014 11. Anghelache, C. et alias (2014). Using Linear and non-linear Models in Macroeconomic Analysis, Revista Romană de Statistică Supliment 1/2014 12. Anghelache, C. et alias (2014). Multiple Linear Regression Models used in Economic Analysis, Revista Romană de Statistică Supliment 10/2014 13. Anghelache C. (2010). Metode şi modele de măsurare a riscurilor şi performanţelor financiar-bancare – Ediţia a II-a, Editura Artifex Bucureşti 14. Arrow, K.J and A.C.Fischer (1974). Environmental preservation, uncertainty and irreversibility, Quarterly Journal of Economics 15. Cremer,J. (1982). A simple proof of Blackwell’s “comparison of experiments” theorem, Journal of Economic Theory 16. Dixit, A.K. and R.S. Pindyck (1994). Investment under uncertainty, Princeton University Press 17. Epstein, L.S. (1980). Decision-making and the temoral resolution of uncertainty, International Economic Review 18. Schlee, E.E. (2001). The value of information in efficient risk sharing arrangement, American Economic Review 19. Treich, N. (1997). Risk tolerance and value of information in the standard portfolio model, Economics Letter 20. Malcolm Gladwell (2013). The Tipping Point. Cum lucruri mici pot provoca schimbări de proporţii, Editura Publica 21. Hăvărneanu, C. Hăvărneanu, G. (2015). Psihologia riscului, Editura Polirom 22. Hirshleifer, J. and J.G.Riley (1992). The analytics of uncertainty and information, Cambridge University Press 23. Silver N. (2013). Semnalul și zgomotul. De ce atât de multe predicţii dau greș-pe cand altele reușesc, Editura Publica 24. Taleb N.N. (2010). Lebăda neagră. Impactul foarte puţin probabilului, Editura Curtea Veche 48 Romanian Statistical Review - Supplement nr. 1 / 2016 Model of Static Portfolio Choices Lect. Mădălina Gabriela ANGHEL PhD. Gyorgy BODO Phd. Student Bucharest University of Economic Studies Okwiet BARTEK, PhD. Student Czestochowa University of Technology Abstract In decentralised economies the financial markets has a key role, being considered as institutions that transfer entrepreneurial risk to consumers. The entrepreneurial risk is assumed by the investors as part of the industrial or infrastructure investment that could be considered as the engine of the economic growth. The risk related to the investments finally is transferred from the investors to the tax paying population which statistically can be considered as risk-averse. The problem of the investors is to determine the optimum balance between the assumed risk and the expected performance, but having a limited investment capital. In this paper we examined a simple version of the problem convincing risk-averse people to accept the purchase of risky assets by receiving an additional premium on it. Also, we focus on behaviour of investors who spend the entire investment at the end of the analysed period, but for simplicity we detach the time component of the equation. Key words: portfolio, asset, choice, model, risk Description of the One-Risky One-Risk-Free Asset Model For the simple understanding of the model we consider an investor (also called agent) who has the capital to invest w0. He can invest part of it (noted with α) in risky assets (e.g. mix of stocks) having the return over the period expressed as random variable and another part (noted with w0 - α) in risk-free assets (e.g. government bonds) with the return over the period r. The investor is interested in maximising the return at the end of period determined by the optimal composition (α, w0 – α) of his portfolio, which can be written as: , (1) where is the final value invested in the risk-free portfolio, and can be described as “excess return” on the risky assets. The problem of investor is to choose α in that way that the Expected Utility (EU) should be maximised: Revista Română de Statistică - Supliment nr. 1 / 2016 49 (2) We can interpret α = 0 as 100% investment in risk-free portfolio, and as α increase, the share of the risky portfolio increases and consequently the investor could accept higher exposure to the risk because of the higher expected net pay-off (earning). Proposition 1. Consider problem (1) where is the optimal investment in the risky assets, and is the excess return of the risky assets over the risk-free rate. The optimal investment in the risky asset is positive if and only if the expected excess return is positive, meaning . Moreover variation of can be interpreted as: (i) if is decreasing, the risk-aversion of the investor is increasing in the sense of Arrow and Pratt; (ii) if is increasing than the risk-aversion is decreasing, meaning the investor can accept higher risk. This is a simplified model which nod details the case why large proportion of population is risk-averse and does not hold any shares of stocks. This might be explained with the fact that obtaining information about the market evolution needs some additional knowledge, and obtaining such knowledge involve personal effort or cost, cost which the (private) investor could consider to high compared with the expected net earnings. Another conclusion we can formulate is related to the fact that riskaverse people hold less risky portfolios, whereas rich people has decreasing risk-aversion and holds larger amount of stocks. Several empirical studies confirm this positive correlation between stock holding and wealth, which offers additional argument in favour of DARA. In a particular case, let assume that the utility function show constant relative risk aversion: for all c, where y is the degree of relative risk aversion. In this condition the problem (2) can be written as: (3) We can observe that the solution of this equation is , where k is a positive constant, such that , leading to the conclusion that under constant relative risk aversion, the optimal amount of investment in risky assets is proportional to wealth. 50 Romanian Statistical Review - Supplement nr. 1 / 2016 Proposition 2. Under constant relative risk aversion, the investors’ willingness to invest in stocks is proportional to their wealth: . We approach an approximate solution for determining the optimal demand by using first-order Taylor approximation to around w, we can approximate as Finally, for the proportion of wealth invested in stocks, we obtain the approximation: (4) where is the relative risk aversion degree evaluated in w, and are respectively the mean and variance of the “excess stock” return. We can conclude, that the optimal share of wealth invested in stock is relatively proportional to the equity premium and inversely proportional to the variance of stock return and relative risk-aversion. In order to sustain the above mentioned conclusion, historical data on assets returns available in USA (Shiller, 1989; Kockerlakota, 1996) shows that the average real return on large part of US stocks was ~7% per year, whilst the average real risk-free rate been ~1%. The Effect of Background Risk Beside the riskiness of assets returns there are also other sources of risk in determining the final wealth of an investor. For example the labour income (wages) are not fully risk-free, and for determine the effect of such risk we can introduce a zero-mean background risk to initial wealth w. This leads to the adjustment of (2) resulting: , (5) Intuition might suggest that , but we want to identify any kind of correlation between them which can influence the investors’ decision making behaviour. The effect of could be considered as “bad-luck” of an investor, therefor they would try to compensate the extra risk with a more precautious behaviour compared to . We can rewrite the (5) considering this approach as: , (6) where value of the function v is defined by for all z. In order to identify the difference we have to compare the function u with Revista Română de Statistică - Supliment nr. 1 / 2016 51 v, in condition assumed that (2)). This is true if (defined in (6)) is smaller than , for all such that , where (defined in (7) , which is equivalent to requiring . Proposition 3. (i) Any zero-mean background risk reduces the demand for other independent risks. (ii) Absolute risk aversion is decreasing and convex. We have to prove that (ii) is sufficient for condition (i). Noticing A(.) the absolute risk aversion, than we can write: . Since we assumed that the absolute risk aversion is decreasing, results that the right hand side of this equation is larger than . In addition, due to the fact that A is considered convex than results is larger than . These three observations together implies condition (7) which is necessary and sufficient for property (i). Portfolio of Risky Assets The above mentioned model can be further refined in sense that the risky assets could be split in two or more sub-portfolio, in this way the investors distribute the risk related they risky investments but also the earnings are distributed between the different portfolios. Let assume for simplicity that these two assets have the same distribution of returns and . In order to determine the optimal structure of their portfolio, we should solve the following program: , (8) where is the amount invested in the first risky asset. Since we consider the investor as risk-averse, the first-order condition is: , The unique rot of this equation is , meaning that for riskaverse investors, the optimal investment is to perfectly balance their investment. This result, which is called also as risk-diversification, is reflected also by the fact that is distributed as , 52 Romanian Statistical Review - Supplement nr. 1 / 2016 where This means that the return of any portfolio is distributed as return of the balanced portfolio plus a pure noise which satisfy the condition: . This conducts us to the conclusion that accepting unbalanced portfolio is equivalent to accepting zero-means lotteries. Further, in real words investors tend to more diversify their portfolio, which is to dislike zero-mean risk. As conclusion, based on theoretical approach, portfolio management is a simple problem, in which investors should not spend too much time and energy. Theoretically we assumed that financial markets are informationally efficient, meaning that the same information about the risk is available for all investors, and investors have mean-variance preferences. Investing more in risky assets investors expect higher return, and by diversifying the risky portfolio they looking for the optimum balance between assumed risk and expected returns. References 1. Adam, Al.; Houkari, M.; Laurent, J.P. (2008). Spectral risk measures and portfolio selection, Journal of Banking & Finance 32, pp. 1870–1882 2. Anghel M.G. (2013). Modele de gestiune şi analiză a portofoliilor, Editura Economică, Bucureşti 3. Anghel M.G. (2013). Theoretical Concept relating to the Management Portfolio, Revista Română de Statistică – Supliment/Trim III, pg. 122 – 126 4. Anghel, M.G. (2012). Statistical indicators used in the analysis of portfolios of financial instruments, Proceedings of the International Symposium “Romanian Economy in the Globalization Conjuncture On Crisis Background”, Revista Română de Statistică – Supliment, pg. 117 – 120 5. Anghelache C., Anghel M.G. (2014). Modelare economică. Concepte, teorie şi studii de caz, Editura Economică, Bucureşti 6. Anghelache C., Anghel M.G. (2015). Econometric models utilized for the portfolio selection, Romanian Statistical Review – Supplement, No. 4, pp. 19-21 7. Anghelache G.V., Anghel M.G. (2013). Specific patterns in portfolio analysis, Theoretical and Applied Economics, Volume XX, No.11, pg. 7 – 24 8. Hagstromer, B.; Binner, J.M. (2009). Stock portfolio selection with full-scale optimization and differential evolution, Applied Financial Economics, ISSN 0960– 3107 print/ISSN 1466–4305, pp. 1559–1571 9. Li, J., Smetters, K. (2011). Optimal portfolio choice with wage-indexed social security, Working Paper 17025 10. Lucas, A.; Siegmann, A. (2008). The Effect of Shortfall as a Risk Measure for Portfolios with Hedge Funds, Journal of Business Finance & Accounting, 35(1) & (2), pp. 200–226, 0306-686X 11. Merton, R.C. (1969). Lifetime portofolio selection under uncertainty: the continuous-time case, Review of Economics and Statistics 51: 247-257 12. Mossin, J. (1968). Optimal multiperiod portfolio policies, Journal of Business, 41:215-229. Revista Română de Statistică - Supliment nr. 1 / 2016 53 Model of Static Portfolio Choices in an ArrowDebreu Economy Prof. Gabriela Victoria ANGHELACHE PhD. Bucharest University of Economic Studies Lect. Mădălina Gabriela ANGHEL PhD. “Artifex” University of Bucharest Gyorgy BODO Phd. Student Bucharest University of Economic Studies Abstract The financial markets and the institutions which are operating in these markets are essential for the good functioning of any decentralised economy. Beside the simplest approach where there is no time dependence, and investment opportunities are limited only to bonds (risk-free assets) or stocks (risky assets) in a more complex economic model we can consider other investment opportunities too. Key words: options, return, portfolio, result, asset One, frequent used case, is to bet on a future event which has a probability to happen or not and at the end to collect a premium (return) if the event occurs. All this, in return of a down payment of a lump sum fee paid ex ante. As example we can mention any kind of sport bets, where you pay an initial fee, bet on an event (e.g. who will win a game, or which will be a final score a.s.o.), and collect a much higher premium if the result you bet it finally occurs. Or, any lottery type game having a similar approach, you buy a ticket, put some numbers and if your bet was correct, you will collect a high premium on it. For example, from economic perspective suppose you can find a counterparty that is willing to pay you a premium if some specified assets will have a return higher than an expected value, or some macroeconomic indicator will work better than initially estimated in a certain moment in time. Also, since the 1980’s it was developed new type of asset called “options” which offers highly nonlinear return for the holders. Other case might be when investors are opting to purchase a “portfolio insurance”, where the minimum return is guaranteed them. A more complete modelling of the financial markets, where several investment opportunities are considered simultaneously for each of them investors assuming a certain risk factor, theory which was first analysed by Arrow (1953) and Debreu (1959). 54 Romanian Statistical Review - Supplement nr. 1 / 2016 Arrow-Debreu Securities and Arbitrage Pricing The full model consider that are finite number of possible S states of nature at the end of analysed period (s = 0, 1, …, S-1), but for the simplicity and easy understanding of the model we consider only two states (S = 2). We assume that the market is complete, meaning that for each possible state (s = 0, 1) there is an asses which provides a unit payoff to the investor if and only if state s occurs and zero otherwise. These are called “Arrow-Debreu securities”. We considered the initial price of the risky asset normalised to 1, therefore the final value of the asset in state s is , meaning that the net asset return is in state 0, and in state 1. For the more complete modelling of a financial market, we take into consideration also a risk-free asset with return r in both states analysed above (s = 0, 1) and let assume that , as an equilibrium condition. For the Arrow-Debreu security associated with state s = 1 we can state that, where α is the number of purchased risky-assets, and B is the risk-free rate used for borrowing. There are two constraints: first, that in state 0 nor revenue from this portfolio for the investor and second, that revenue in state 1 is one. Than the solution of this system is and . The similar approach can be performed for the Arrow-Debreu security associated with state s = 0. This two states reflect that there are two independent assets to consider all set of possible risks, but the model can be extended to more general case with many states. By investing in the appropriate portfolio of Arrow-Debreu securities investors can structure any set of state-contingent claims. (for keep simple the understanding of the A-D model, this case is not discussed in this paper). If we note the price of the Arrow-Debreu security, than the price associated with state s = 1 can be expressed with: By a simple arbitrage argument, must be equal to the cost of building the respective portfolio –purchasing α units of the risky assets Revista Română de Statistică - Supliment nr. 1 / 2016 55 whose price is 1, from which we must deduct a loan of B. Let assume that , than the Arrow-Debreu security associated with state s = 1 is a call-option with strike price . This mechanism of the arbitrage pricing can be further refined in more complex environment, as Black and Scholes (1973) accomplished. With this simple approach we wanted to show, how to obtain the price of this call-option from the characteristic of the risk of its underlying asset, but also the inverse is valid, once we know the price for the Arrow-Debreu securities we can calculate the price of any financial assets. One particular case is a risk-free bond with a payoff of 1 in each state of nature, when the price of the bond must be the discontinued value of one unit of wealth received with certainty at the end of period: Furthermore, having a portfolio of S assets consisting of one unit of each and every Arrow-Debreu security also would have a risk-free payout of 1. Therefore using no-arbitrage arguments, we must have Let define , which is usually referred as the risk neutral probability for state s. By this interpretation, we can see that the price of any asset in a complete market is its expected value, discounted as risk-free rate. Optimal portfolios of Arrow-Debreu Securities We noted cs the investment in the Arrow-Debreu security associated to state s, the problem of the investor having an initial wealth w, is to determine the demand for each of the Arrow-Debreu securities in order to set-up an optimal portfolio. The program of the investor can be written as where the only restriction is related to the budget constraint . In other words, if we could consider S number of different goods, where is the price of good s, the problem of the investor is that under the budget constraint how to select a bundle that maximises his utility 56 Romanian Statistical Review - Supplement nr. 1 / 2016 For optimality, the following first-order conditions are both necessary and sufficient where ξ is the Lagrange multiplier associated to (2), equal to the marginal utility of additional wealth. The optimal consumption depends depend from the state s only through the ratio of the state price per unit probability. In the particular case of S = 2, if there are two states with the same price per unit of probability for the associated contingent claims, the optimal investment is to buy the same quantity of these claims. Simple graphical illustration For a simple understanding of the model we return to the particular case of two states nature S = 2, and we assume that the risk-free rate is zero (r = 0) in order to focus on the risk aspects of the model. The set of claims {( )} presented in the graph, for which , represent an isoexpected-value locus (i.e. set of claims with mean wealth ), which actually represent the budget constraint of the investor. We set the EU (expected utility) equal to constant k in order to analyse investor’s preferences, than results that which is shown as a curve in the diagram. The set of claims for which EU is constant defines an indifference curve in state-claim space . We notice that the 45o line is referred as the certainty line, and represents the locus of claim with equal consumption in both states of world. Revista Română de Statistică - Supliment nr. 1 / 2016 57 From the above, we observe that is the marginal rate of substitution (MRS) between states. It is the rate of trade-off, at the margin, between one unit of consumption in state 0 and consumption in state 1 for which the investor is just indifferent. This lead to the conclusion that individuals are neutral to the introduction of small risk. Finally we can conclude that in a complete economy with no arbitrage opportunity, there exists a “risk-neutral” probability distribution in a way that the price of any asset can be expresses as the discounted value of the riskneutral expectation of its future payoffs. References 1. Ameur, H.B.; Prigent, J.L. (2010). Behaviour towards Risk in Structured Portfolio Management, International Journal of Economics and Finance Vol. 2, No. 5, pp. 91-102 2. Anghel M.G. (2013). Modele de gestiune şi analiză a portofoliilor, Editura Economică, Bucureşti 3. Anghel M.G. (2013). Identification of financial instruments – important step in building portfolios, Romanian Statistical Review, No. 9, pg. 52 – 65 4. Anghel M.G. (2013). Model of Portfolios Analysis, Revista Română de Statistică – Supliment/Trim IV, pg. 39 – 43 5. Anghel M.G. (2013). Concept of the Portfolio Management, Romanian Statistical Review – Supplement/December – „Statistical-Econometric Models Used in the Study of Economic Variables Evolution” 6. Anghel, M.G. (2011). General aspects about management portfolios of financial instruments, ART ECO - Review of Economic Studies and Research, Nr.3 7. Anghelache C., Anghel M.G. (2014). Modelare economică. Concepte, teorie şi studii de caz, Editura Economică, Bucureşti 8. Anghelache, C. (coord.), 2014. Statistical-Econometric Models Used To Studyb The Macroeconomic Correlations, Romanian Statistical Review-Supplement, December 2014. 9. Baule, R. (2010). Optimal portfolio selection for the small investor considering risk and transaction costs, OR Spectrum, v. 32, iss. 1, pp. 61-76 10. Gans, J.; Murray, F.E. (2011). Funding scientific knowledge: selection, disclosure and the public-private portfolio, Working Paper 16980 11. Kopman, L.; Liu, S.S., Shaw, D. (2009). Using Lagrangian Relaxation to Obtain Small Portfolios, The Journal of Portfolio Management, pp. 75-79 12. Samuelson, P.A. (1963). Risk and uncertainty: the falacy of the Law of Large Numbers, Scientia 98: 108-113. 58 Romanian Statistical Review - Supplement nr. 1 / 2016 Portfolio Management and Predictability Prof. Gabriela Victoria ANGHELACHE PhD. Bucharest University of Economic Studies Prof. Vladimir MODRAK, PhD Technical University of Košice Lect. Mădălina Gabriela ANGHEL PhD. “Artifex” University of Bucharest Marius POPOVICI PhD. Student Bucharest University of Economic Studies Abstract Future developments of states and states of nature of a system are predictable. Portfolio management needs predictability techniques In order to benefit of opportunities. In theory, predictability has no time dimension. Practically, as opportunity is embedded stochastically, there may appear changes of state that are predictable, as in the correlation between returns and stocks. A certain resource reversion might be possible with regard to returns and stock. The predictability of the optimal portfolio management becomes the objective of any investor who follows a flexible strategy based on optimal exposure to risk. Thus, investors will try to anticipate the possible shocks affecting the opportunity set of their investment. More precisely, they will admit the possibility to hedge any bad news concerning the future opportunity set, the so called “myopia” relative to time horizon when predictability is possible. This circumstance is part of the relative risk aversion. We can affirm that predictability has the same effect as a reduction of risk aversion. Key words: Portfolio management, predictability, risky assets, hedging demand, planning horizon, conditional distribution, marginal value of wealth. Introduction Predictability represents the ability of an entity or system to value the future developments of the states of nature of a system. Especially with regard to portfolio management, predictability is important as a possibility to detect future laws of the decision process, but also measures to be taken in order to re direct the evolution of management process. Predictability is closely related to opportunity concept, and in correlation with management portfolio it has to do with time management too. Theoretically, a problem of portfolio management will deal with a settled opportunity which is non-variable in time. In the real world, the opportunity is settled in a stochastic manner, with some changes of state being Revista Română de Statistică - Supliment nr. 1 / 2016 59 predictable. For example, predictability is possible in case of the correlation between incomes and stocks. For example, there is a kind of reversibility of resources in case of returns relative to stocks and it was lately accepted. Thus, a large cash-in generated by a risky portfolio implies a smaller cashin tomorrow. In other words, good news of today will bring bad news in the future regarding opportunities. Predictability and the optimal dynamic portfolio When exposing themselves to risky decisions, investors follow a flexible strategy. This is part of the process of opportunity organizing. They will try to anticipate any shock inside the opportunity set. More precisely, they will admit the possibility to hedge any bad news concerning the future opportunity set. This process is not a difficult one if shifts made are statistically correlated with the current returns. The demand for stocks due to this anticipation is called “hedging demand” for stocks. As stocks are considered safer on long terms than on short terms, intuition suggests that an investor with a longer planning horizon will take more risks early in life than one with a shorter planning horizon. In order, to simplify this picture we will limit our analysis to the constant relative aversion to risk with a time horizon of two periods. Constant relative risk aversion implies myopia relative to time horizon when predictability is not possible. Suppose the economy has one risk-free asset with zer return, and one risky asset in period t with return denoted by x̃t, t = 0,1. The opportunity set in the second period comes from the supposition that distribution of x̃1 is correlated with x̃0. Suppose that Ex̃0 . Investors invest only with regard to retirement at the end of the second period, with no intermediary consumption. In order to evaluate the first period optimal demand for the risky asset, particularly the hedging component, we have to follow a certain method. We will begin by solving the problem that confronted the investors in the second period for each possible situation. The news are related to not only the wealth z accumulated by that time, but also by the return generated by the risky asset x0 in the first period. More exactly, the value function v is defined by (1) The optimal solution for this program is a separable function 1(z,x0) = a(x0)z. This implies that the value function is separable, with v(z,x0) = h(x0) z1- γ/(1- , where h(x0) = E[(1 + a(x0)x̃1)1- γ | x0]. 60 Romanian Statistical Review - Supplement nr. 1 / 2016 Let’s get back to the first period decision problem. This can be written as (2) In order to determine the hedging component for risky assets demand, we compare 0* to the demand for risky assets when there is no predictability, for example when x̃1 is independent of x̃0. In this specific case, we know that myopia is optimal. Thus, in the absence of any predictability, investors have to solve the following: When returns are somehow predictable, the hedgind demand is 0*m. The hedging demand will be positive if the derivative of H evaluated at 0 m 0 is positive. In other words, anytime E [x̃0 ( w0 + 0m x̃0 ) 1- γ]=0. In order to evaluate a specific type of predictability, let us examine the case of an increase in x0 which deteriorates the distribution of x̃1 in sense of first order stochastic dominance (FSD). We have a special case when the stochastic process (x̃0, x̃1) indicates a mean-reversion. Suppose the conditional distribution of x̃1 may be written as x̃1| x̃0= - x0+ , where is considered to be independent of x̃0 and where k is a positive scalar. As any shift of FSD in x̃1 diminishes final wealth EU, this assumption implies that 0 is 1γ negative. Since v(z,x0) = h(x0)z /(1- γ), it results that h’ should be negative when γ <1, and positive when γ>1. Suppose that relative risk aversion γ is larger than unity. As h’ should be positive in this case, it follows that for all x0, . Considering the expectations for both sides, it follows in turn that . Thus, the hedging demand is positive when the relative risk aversion is larger than unity. In case we have a relative risk aversion less than unity, γ <1, than h’ is negative and the inequality is reversed. This result is rezumed in the following proposition: Suppose the increase of return in the first period deteriorates the distribution of return of the second period in the sense of the first-order Revista Română de Statistică - Supliment nr. 1 / 2016 61 stochatic dominance. Then, the hedging demandf or risky asset is positive (respective negative) if constant relative risk aversion is larger (respectively smaller) than unity. An other interpretation of this result is the following: when the relative risk aversion is constant and larger than unity, a longer time horizon should induce the wish of investors to take more risks. The contrary is true also if the relative risk aversion is smaller than unity. Note the fact that when investors have a logarithmic utility function (γ =1) myopia is still optimal in the presence of predictability. The choice of an initial risk portofolio is dictated by the fall of the marginal value of wealth at the end of the first period. This wealth marginal value depends on the future opportunities set. In case predictability reduces the wealth marginal value in its abundence states, making it increase where it is low, then predictability has the same effect as a reduction of the risk aversion: it raises the risk optimal level in the portfolio. as consequence, we observe that the next step of the analysis is to determine the effect of the FSD deteriorating-shift in the return of the risky asset will have on the marginal value of wealth. In the special case of mean reversion, we have two different effects of x0 increase. The first effect is the effect of wealth: as the return expected in the second period becomes smaller , the same is going with wealth, which becomes smaller. This event raises the wealth marginal value, while v is concave in z. The second effect is a precautionary effect: investors will invest less in the risky asset thus reducing the risk exposure. Under prudence, this reduces the wealth marginal value. The global effect of an increase in x0 of the marginal value of wealth is ambiguous. When the relative risk aversion is constant and larger unity (and this happens if and only if the absolute prudence is smaller than twice the absolute risk aversion, that explains why this condition implies the fact that precautionary effect is dominated by the wealth effect), then the wealth effect will be always dominant against the precautionary effect, and the hedging demand is positive. When the relative risk aversion is less then unity, the wealth efect is dominated by the precautionary effect. Conclusions Risky decision regarding portfolio are implied in management activities with regard to stocks. Let’s consider that an opportunity set is important for any investor who manages risky assets. In theory, predictability was not an aspect of interest given the lack of any time horizon assumption. In every day life and experience, opportunity and its management is possible and it explains the changes of states of any process in stochastic expression. Thus some changes of state might be predictable with regard to return and stocks’volume correlation. The predictability of the optimal portfolio management becomes the objective of any investor who follows a flexible strategy based on optimal exposure to risk. Thus, investors will 62 Romanian Statistical Review - Supplement nr. 1 / 2016 try to anticipate the possible shocks affecting the opportunity set of their investment. More precisely, they will admit the possibility to hedge any bad news concerning the future opportunity set, the so called “myopia” relative to time horizon when predictability is possible. This circumstance is part of the relative risk aversion. When relative risk aversion is larger than unity, hedging demand for risk assets is positive, that means that the time horizon is larger making investors to wish to invest more risky. When the relative risk aversion is less than unity, hedging demand for risky assets is negative, while time horizon is smaller. In presence of predictability, myopia is the optimal solution of the investors. Considering all these, we can affirm that predictability has the same effect as a reduction of risk aversion. References 1. Anghel M.G. (2015). Correlation between BET Index Evolution and the Evolution of Transactions’ Number – Analysis Model, International Journal of Academic Research in Accounting, Finance and Management Sciences, Volume 5, No. 4, October 2015, pg 116-122 2. Anghel M.G. (2013). Modele de gestiune şi analiză a portofoliilor, Editura Economică, Bucureşti 3. Anghel M.G., Lixandru G. (2013). Classical Models used in the Management of Financial Instruments Portfolio, Revista Română de Statistică – Supliment/Trim III, pg. 208 – 211Baule, R. (2010) – Optimal portfolio selection for the small investor considering risk and transaction costs, OR Spectrum, v. 32, iss. 1, pp. 61-76 4. Anghelache C., Anghel M.G., Manole A. (2015). Modelare economică, financiarbancară şi informatică, Editura Artifex, Bucureşti 5. Anghelache C., Anghel M.G. (2014). Modelare economică. Concepte, teorie şi studii de caz, Editura Economică, Bucureşti 6. Anghelache C., Anghel M.G. (2014). Using the regression model for the portfolios analysis and management, Theoretical and Applied Economics, Volume XXI, No.4, pg. 53 – 66 7. Anghelache, C., Anghel M.-G. 2014. The Model of W.F. Sharpe and the Model of the Global Regression Used for Portfolio Selection, Revista Romană de Statistică Supliment 7/2014. 8. Benjamin, C.; Herrard, N.; Houée-Bigot, M.; Tavéra, C (2012). Forecasting with an Econometric Model, Springer 9. Buraschi A.; Porchia, P.; Trojani, F. (2006). Correlation Risk and Optimal Portfolio Choice, AFA New Orleans Meetings Paper 10. Considine G. (2009). Projecting portfolio risk and return, Managerial and decision economics 11. Kini, O.; Mian, S.; Rebello, M.; Venkateswaran, A. (2009). On the Structure of Analyst Research Portfolios and Forecast Accuracy, Journal of Accounting Research Vol. 47 No. 4, Printed in U.S.A., pp. 867-909 12. Pagliacci M.G.R., Anghel M.G., Sacală C., Anton V.L.(2015). Some Models used for setting up the Futures Price, Romanian Statistical Review - Supplement/No. 6, pg. 72 – 78 13. Samuelson, P.A. (1989). The judgement of economic science on rational portfolio management: indexing, timing and long horizon effects, Journal of Portfolio Management (Fall issue):3-12 14. Snowberg, E.; Wolfers, J., Zitzewitz, E. (2012). Prediction markets for economic forecasting, Working Paper 18222 Revista Română de Statistică - Supliment nr. 1 / 2016 63 Significant Aspects of Investment Dynamics Prof. Gabriela Victoria ANGHELACHE PhD. Bucharest University of Economic Studies Lect. Mădălina Gabriela ANGHEL PhD. “Artifex” University of Bucharest Marius POPOVICI PhD. Student Bucharest University of Economic Studies Abstract The myth of different choices regarding portfolio composition by taking risks on the long term and on the short term follows to be busted. Whether the opportunity of taking risky decisions regarding investments in the future influences or not short term decisions on risky investiments, especially when retirement is envisaged, always represented a dilemma for those with a short span of life but still investing. Problems like intermediary consumption, exposure to risky investments or differences between young and old investors’ portfolio and time horizon issues, are going to be investigated in this article, finding a solid answer by means of mathematical modelling. Key words: taking risk, planning horizon, risky stock, investment problem, absolute risk tolerance, budget constraint, state price per unit of probability, risk-free rate, investment dynamics Introduction The problem of the way the opportunity of taking risks in the future influences the will of takings risks in the short run is important taking into account various situations, especially when making decisions such as investments targeting retirement. This is a special case of investment activities when intermediary consumption is lacking. Intermediary consumption is present otherwise in all other kinds of investment circumstances scientifically known as ”investment problem”. This is when the investor’s objective is represented by the intention of maximizing the EU of his wealth - already accumulated, at a specific date. Of course, the concept of intermediary consumption will be introduced later in this article, as well as the idea that risks are independent over time. Taking risks and wealth management Let’s consider a general situation that allows us to issue general conclusions regarding this topic. Let us admit the existence of an investor having a certain 0 accumulated wealth, and who lives for two periods of 64 Romanian Statistical Review - Supplement nr. 1 / 2016 time. When each of these periods starts, the investor has the opportunity to invest taking certain risks. Later, at the end of each period, he will observe the effects of his investment decisions. It is important to see that he will observe the effects of his investing decions in the first period before being able to decide how to take risks for the second period. This will be the source of a certain dynamics of the respective problem, generating a flexibility which is essential for risk management. Evidently, investors will take less risks in the second period in case they lost a large amount of their investments in the first period. This circumstance of second period problem finds a perfect expression in Arrow-Debreu portfolio decision which we assume. Suppose that we have S possible states of nature s = 0 ,..., S – 1. There is an uncertainty dominating the second period described by the probability vector (p0,... , ps-1). Considering Пs as the unit price of Arrow-Debreu security associated to state s, we also assume that the rate of risk free is zero. This means that a claim paying one lei in each state of nature should itself cost one lei: So to speak, the investor who doesn’t take any risk in the second period will end by having the samefinal wealth as at the end of the first period. Having the wealth z, this investor will choose the portfolio (c0, ... , cs-1) maximizing EU of his wealth at the end of the period when his budget was constraint: (1) This is equivalent to (2) when 1 = (c0,...,cs-1). During zero period, the investorhas to take a risky decision 0 with a payoff z( 0,x) depending on the realization x of some random variable x̃ . This might be another problem of portfolio choice. We are able to obtain the optimal exposure to risk in zero period by using the following program: (3) In order to determine the opportunity of taking risks in the second period based on the optimal exposure to risk in the first period, we have to compare the solution obtained previously with theoptimal exposure to risk in the first period when there is no such an option to take further risk in the a Revista Română de Statistică - Supliment nr. 1 / 2016 65 following period. Such an investor with a short span of life similarly to the myopic investor would selecy level 0 able to maximize the EU of z( 0,x̃ ): (4) Given these two programs (3) and (4) based on two periods and on only one period, we can see the difference between them: the utility function u in the first program is replaced by the value function v. By this, the effect of the future is completely comprised in the value function. For our circumstances, we can say that the opportunity to take risk for the future make our willingness to take risks todayonly if v is less concave than u with regard to Arrow-Pratt. And this is a consequence of the case of a one-risky-one-risk-free portfolio problem we already presented above. We can affirm that the optimal exposure to risk in the first period is larger than the myopic exposure in the value function v defined by program (1) is less concave than the original utility function u. In this case, the tolerance degree to absolute risk of v is more risk tolerant than u. The degree of absolute risk tolerance of v can be defined by the following proposition: ”The value function for Arrow-Debreu portfolio problem (1) has a degree of absolute risk tolerance by the following equation: (5) where c* is the optimal solution to the problem (1) and T(·)= -u’(·)/u’’(·) represents the absolute tolerance to risk for final consumption”. As a proof of this proposition: The optimal solution to the program (1) is denoted c*(z). It has the property of satisfying the following condition of first-order: er: (6) where s = Пs/ps represents the state price per unit of probability and ξ(z) is the multiplier associated with (1) for a particular value of z. The condition of a complete differentiating (6) regarding z and eliminating s is given by: (7) A diffentiating budget constraint will result in turn in: 66 Romanian Statistical Review - Supplement nr. 1 / 2016 (8) By replacing cs*’(z) by its expression in (8)it will follow that (9) Finally, by fully differentiating v(z) , which by definition equals u(c *(z)),we obtain s s v’(z) su’(cs*(z))cs*’(z) s*’(z) What we obtained by associating the Lagrange multiplier to the budget constraint of the consumer equals the shadow price of wealth. From this result we can see that v’’(z) and Tv(z) (z)/ The proposition follows immediately from equation (9). We accept that the risk-free rate is zero, in turn implying that this eliminates a potential effect of wealth for those who allow themselves to invest in the second period. In this case, property (5) states that the absolute risk tolerance of the value function is a weighted average of the risk tolerance degree of the final consumption. This property allows us to compare the degrees of concavity of v and u. For example, suppose that u exhibits „hyperbolic absolute risk aversion” (HARA), i.e. that T is linear in c. This implies that Tv(z)= s*)=T( s*)=T(z). Thus, when u is in HARA, the value function v has the same degree of concavity as u: v(·) = Ku(·). The consequence is that the two programs have exactly the same solution. In other words, the option to take risk for the future has no effect on exposure to risk today. In this case, myopia is optimal. Ceteris paribus, the young and old investors should select the same portfolio composition. Assuming that the opportunity of future risk taking will raise the tolerance to current risks. in order to do this, we suppose that the utility function u expresses a convex absolute risk tolerance, and by applying Jensen inequality, it follows that Tv(z)= s*)= T( s*)=T(z). A nonnegative T” is compatible with the intuition that a longer time horizon might induce a supplementary risk taking which may be found in the fourth derivative of the utility function. On the other hand, with a T” non-positive, a longer time horizon for investment should imply a more conservative on short term. A second proposition is „Suppose that risk-free rate is zero. In the dynamic Arrow-Debreu portfolio problem with serially independent returns, Revista Română de Statistică - Supliment nr. 1 / 2016 67 a longer time horizon raises, respectively reduces the optimal risk exposure on short term if the risk absolute tolerance T(·) = -u’(·)/u’’(·) is convex, respectively concave. In HARA, time horizon has no effect on optimal portfolio”. When the long term investment is targeted for consumption at a certain date, whether the investor is supposed to modify his own exposure to risk as the time horizon recedes is an empirical question which relies on convexity, linearity or concavity of absolute risk tolerance. Of course, none of these conditions should be maintained for all wealth levels. A tolerance to risk is possible which sometimes is convex, other times is concave. We cannot be able to envisage or predict the effect for a longer planning time horizon with regard to an investment strategy. Depending on circumstances, this investor might invest more money in stocks, other times he might invest in bonds more than would be invested under myopia. One might think about convexity/concavity of absolute tolerance in terms of introspection. Remember that the amount invested in stocks is approximately proportional to T. It is about a wealth raise. Question is if we have to do with an increase at an increasing rate as wealth increases. If it is so, this might mean a convex T and a positive effect for the opening of the time horizon while taking risks. It is possible that one to assume that this is a forced assuming in order to simplify things, and not for the realism of approach. Econometric tests for HARA are rare as literary resources. Conclusions Under HARA preferences, choosing to take risk in the future has no effect on optimal exposure to risk today. That comes to the fact that myopia in investments is optimal. The opportunity of taking risks in the future raises the tolerance to the current risks. A risk tolerance non-negative might be compatible with the intuition the longer time horizon is, the bigger need of taking risk will be. On the other hand, when risk tolerance is non-positive, a longer time horizon for investments should imply a more conservative investment on the short run. Also, when the long run investment is targeted for consumption at a certain date, it is a matter of concavity, convexity or linearity of absolute risk tolerance for the investor to modify his exposure to risk as the time horizon recedes. None of these conditions should be applied to all levels of wealth. Sometimes, the tolerance to risk is concave, other times it can be convex. For such cases and individuals, it is not possible to predict the effect of a longer planning horizon with regard to investment strategies. His investments will be directed sometimes to stocks, sometimes to bonds, somehow chaotic, than would be invested under myopia. Generally, myopia is considered optimal simplifying the analysis. 68 Romanian Statistical Review - Supplement nr. 1 / 2016 References 1. Anghelache, C. , Anghel, M.G., Manole Alexandru (2015). Modelare economică, financiar-bancară şi informatică, Editura Artifex, Bucureşti 2. Anghelache, C. , Anghel, M.G. (2015). Statistică. Teorie, concepte, indicatori şi studii ce caz, Editura Artifex, Bucureşti 3. Anghelache, C., Anghel, M.G. (2015). Main Aspects Regarding Some Non-Linear Models Used in Economic Analysis/ Revista Romană de Statistică Supliment/ Romanian Statistical Review Supplement, 9/2015: 3-11 4. Anghelache, C., Anghel, M.G. (2015). Theoretical Aspects Concerning the Use of Statistical-Econometric instruments in Analysis of Financial-Assets, Revista Romană de Statistică Supliment /Romanian Statistical Review, 9/2015:39-49 5. Anghelache C., Anghelache G.V., Anghel M.G. et al (2014). The International Trade Evolution, Revista Romanian Statistical Review - Supplement/No. 1, pg. 84 – 87 6. Anghelache C., Manole A., Anghel, M.G. (2014). The business environment and the foreign investment, Romanian Statistical Review - Supplement/No. 10, pg. 7 – 14 7. Anghelache, C., Pagliacci, M., Mitruț, C. (2014). Econometric Analysis through the Use of Statistical-Econometric Models, Romanian Statistical Review Supplement 4/2014. 8. Anghelache, C., Anghelache, G.V., Partachi, I., Stanciu, E., Dragomir, B. (2014). Macroeconomic Analysis based on Econometric Models, Revista Romană de Statistică Supliment 10/2014. 9. Anghelache C., Anghelache G.V., Nan S., Anghel M.G. (2012). Evoluţia produsului intern brut şi a investiţiilor străine directe în ultimul deceniu, Revista Română de Statistică – Supliment Trim.1, pg. 198-204 10. Barberis, N. (2000). Investing for the long run when returns are predictable. Journal of Finance, 55:225-264. 11. Biji, E. M., Lilea, E., Vatui, M., Gogu, E. (2012). Compendiu de statistica aplicata în economie, Oscar Print Publishing House, Bucharest. 12. Carroll, C.D. (1997). Buffer-stock saving and the life cycle/permanent income hypothesis, Quarterly Journal of Economics, 112:1-55 13. Deaton, A. (1991). Saving and liquidity constraints, Econometrica, 59:12211248. 14. Detemple, J.B. (1986). Asset pricing in an economy with incomplete information. Journal of Finance 61:383- 392 15. Samuelson, P.A. (1969). Lifetime portfolio selection by dynamic stochastic programming, Review of Economics and Statistics 51:239-246 16. Voineagu, V., Ţiţan, E şi colaboratorii (2006). Teorie şi practică econometică, Editura Meteor Press, Bucureşti Revista Română de Statistică - Supliment nr. 1 / 2016 69 Model for estimating Financial Results at Company Level by using Simple Linear Regression Andreea-Gabriela BALTAC PhD. Student Zoica DINCA (NICOLA) PhD. Student “Artifex” University Bucharest, Bucharest University of Economic Studies Abstract The econometric model means a lot of numerical relations that enables simplified representation of the economic process subject to study. Current models often involves more than ten relations (equations). The validity of a model is tested by comparing the results with statistical observations. In order to study an economic phenomena, he is represented through a variable behavior. This economic variable depends on other variables of which is linked through mathematical relationships. The regression analysis is a statistical technique based on the identification of two variables and is used to relate variables. Key words: econometric model, simple linear regression, evolution, variables, analysis, performance, estimation. By using the simple linear regression, we predict scores on one variable from the scores on a second variable. The variable we are predicting is called the criterion variable and is referred to as Y. The variable on which we are basing our predictions on is called the predictor variable and is referred to as X. When there is only one predictor variable, the prediction method is called simple regression. In simple linear regression, the topic of this section, the predictions of Y when plotted as a function of X form a straight line. In 1990, Bowerman et al, have revealed that the regression analysis is a statistical technique based on the identification of two variables and is used to relate variables. The purpose of this model is to build a mathematical model to relate dependent variables to independent variables. In 1981, Draper and Smith, defined a regression model as a single algebraic equation, which was the next form: 70 Romanian Statistical Review - Supplement nr. 1 / 2016 Z = f (X1, X2,…. Xk) + u where: Z = is a dependent variable than can be explained by the variables (X1, X2,…. Xk) u = is a random variable Comparing the results with statistical observations is the way in which the validity of a model is tested. An economic phenomena, in order to study, is represented through a variable behavior. This economic variable depends on other variables of which is linked through mathematical relationships. The regression analysis is a statistical technique based on the identification of two variables and is used to relate variables. In this article, my purpose is the analysis of two variables by using the simple linear regression. I have made a model of simple linear regression used in measuaring financial performance. The purpouse is the estimation of the financial results frm TRANSGAZ S.A. during 11 years, meaning the period 2004-2014, through simple linear regression method. In order to highlight practically how to use simple linear regression analysis of financial results at Transgaz S.A., I will start from a series of data on the evolution of the turnover and personnel expenses over the past decade. The evolution of turnover and personnel expenses durring 2004 – 2014 Table 1 (thousand lei) Year 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Turnover 772.277.441 770.115.066 909.017.487 1.038.866.794 1.119.389.990 1.187.350.293 1.308.103.000 1.336.979.000 1.327.987.000 1.484.710.000 1.618.090.000 Personnel expenses 104.882.303 123.213.024 141.326.802 175.292.350 208.185.701 224.549.127 300.132.000 322.471.000 364.884.840 344.869.390 351.858.470 For each of the 11 years, I have presented the values of the turnover and the personnel expenses, in order to see the influence of the two indicators over the eachother. Revista Română de Statistică - Supliment nr. 1 / 2016 71 The two indicators registered positive values increasing from year to year in the economic analysis. Practically every year of the period investigated, the turnover increase, which also increase the personnel expenses. Since 2004 and untill 2014, the values of the two indicators, turnover and personnel expenses grown from one year to another, which is the result of the performance registred by the economic agent that we have studied. We note that in 2008, the year when the financial crisis unleashed in Romania, we are witnessing a modest turnover growth once again 1,119,389,990 lei, unlike in 2007 when it totaled 1,038,866,794 lei. The following year, 2009, the turnover is still low but positive, being still under the impact of the financial crisis. In 2010, the turnover is 1,308,103,000 lei and reach by 2014, the last year covered a total of 1,618,090,000 lei. For a better view I have also made a graphic representation of the evolution of turnover and personal expenses during 2004-2014. The evolution of turnover and personnel expenses durring 2004 – 2014 Figure 1 From the figure we can see that the evolution of the expenditures of personal development is somewhat similar to that of turnover, showing a strong link between the two variables analyzed. The strong link between the two variables is similar of the developments highlighted by the data series. Through Eviews statistical software for data analysis, we examine the evolution of two variables analyzed to estimate the parameters of the 72 Romanian Statistical Review - Supplement nr. 1 / 2016 regression model. We defined the resultant turnover variable, and the variable factor is the amount of time personnel expenses. Eviews, gaved us information about the values of R-squared, Adjusted R-squared, S.E. of regression, Sum squared resid, F-statistic, Prob (F-statistic). The values are presented in the table below. The evolution of turnover and personnel expenses durring 2004 – 2014 (Eviews) Table 2 C (1) = turnover C (2) = personnel expenses The recorded value of the personnel expenses has a major influence on the development of turnover, any modification at this level increasing the turnover value. Free factor taken into account in the regression model, has a significant influence. Factors that were not taken into account when building econometric model lead to a significant reduction of the turnover. Recorded values of statistical tests R and R2 are close to 85%, respectively 90.64% and R-squared = 90.64% and Adjusted R-squared = 89.61%, which verifies the accuracy of the econometric model considered. We can say that the model is correctly analyzed, showing a level of risk acceptable if an economic analysis. Fot the Test F-statistic value, 87.24677 is higher than reference table (4.6), which emphasizes the idea that the econometric model is properly considered, it can be used in economic analysis and forecasting of the turnover. Revista Română de Statistică - Supliment nr. 1 / 2016 73 Conclusion The econometric model means a lot of numerical relations that enables simplified representation of the economic process subject to study. The regression analysis is a statistical technique based on the identification of two variables and is used to relate variables. In statistics, simple linear regression is the least squares estimator of a linear regression model with a single explanatory variable. Simple linear regression refers to the fact that this regression is one of the simplest in statistics. The purpose of this article, was the analysis of two variables by using the simple linear regression. For this analysis the two variables used where: turnover and personnel expenses. For each of the indicators I have considered, I presented the values during the years 2004-2014. I have made a model of simple linear regression used in measuaring financial performance. The purpouse is the estimation of the financial results frm TRANSGAZ S.A. during 11 years, meaning the period 2004-2014, through simple linear regression method. The model for estimating financial results at TRANSGAZ S.A. by using simple linear regression reveals the existence of a linear connection between the amount of turnover and personal expenses. This points to the possibility of using a linear regression model to study the relationship between the two variables. The results have shown that the value of the personnel expenses has a major influence on the development of turnover, any modification at this level increasing the turnover value. The two indicators registered positive values increasing from year to year in the economic analysis. Practically every year of the period investigated, the turnover increase, which also increase the personnel expenses. Since 2004 and untill 2014, the values of the two indicators, turnover and personnel expenses grown from one year to another, which is the result of the performance registred by the economic agent that we have studied. We note that in 2008, the year when the financial crisis unleashed in Romania, we are witnessing a modest turnover growth once again 1,119,389,990 lei, unlike in 2007 when it totaled 1,038,866,794 lei. The following year, 2009, the turnover is still low but positive, being still under the impact of the financial crisis. In 2010, the turnover is 1,308,103,000 lei and reach by 2014, the last year covered a total of 1,618,090,000 lei. The strong link between the two variables is similar of the developments highlighted by the data series. 74 Romanian Statistical Review - Supplement nr. 1 / 2016 Through Eviews statistical software for data analysis, we examine the evolution of two variables analyzed to estimate the parameters of the regression model. We defined the resultant turnover variable, and the variable factor is the amount of time personnel expenses. Eviews, gaved us information about the values of R-squared, Adjusted R-squared, S.E. of regression, Sum squared resid, F-statistic, Prob (F-statistic). The recorded value of the personnel expenses has a major influence on the development of turnover, any modification at this level increasing the turnover value. Free factor taken into account in the regression model, has a significant influence. Factors that were not taken into account when building econometric model lead to a significant reduction of the turnover. Recorded values of statistical tests R and R2 are close to 85%, respectively 90.64% and R-squared = 90.64% and Adjusted R-squared = 89.61%, which verifies the accuracy of the econometric model considered. We can say that the model is correctly analyzed, showing a level of risk acceptable if an economic analysis. Fot the Test F-statistic value, 87.24677 is higher than reference table (4.6), which emphasizes the idea that the econometric model is properly considered, it can be used in economic analysis and forecasting of the turnover. References 1. Angrist JD, Imbens GW, Rubin DB. ‚’’Identification of causal effects using instrumental variables’’, J Am Stat Assoc 1996; 91:444–455; 2. Anghelache, C. et al., ‚’’Econometrics’’, Artifex Publishing House, Bucharest, 2010; 3. Anghelache, C., ‚’’Treaty on Theoretical and Economical Statistics’’; Economica Publishing House, Bucharest, 2008; 4. Fieller EC, Hartley HO, Pearson ES. ‚’’Tests for rank correlation coefficient’’ I. Biometrika 1957; 44:470–481; 5. Fieller EC, Pearson ES. ’’Tests for rank correlation coefficients’’ II. Biometrika 1961; 48:29–40; 6. Galton F. ’’Correlations and their measurements, chiefly from anthropometric data’’, Proc R Soc London 1888; 45:219–247; 7. Goldman RN, Weinberg JS. ‚’’Statistics: an introduction’’ Upper Saddle River, NJ: Prentice Hall, 1985; 72–98; 8. Hettmansperger TP. ‚’’Statistical inference based on ranks’’ Malabar, Fla: Krieger, 1991; 200–205; 9. Holland P. ‚’’Statistics and causal inference’’’, J Am Stat Assoc 1986; 81:945– 970; 10. Kendall M, Gibbons JD. ‚’’Rank correlation methods.’’, 5th ed. New York, NY: Oxford University Press, 1990; 8–10; Revista Română de Statistică - Supliment nr. 1 / 2016 75 11. Ilic, M., “Economic Value Added As A Modern Performance Indicator, Perspectives of Innovations, Economics & Business”, Volume 6, Issue 3, 2010, www.pieb.cz; 12. Neter J, Wasserman W, Kutner MH. ‚’’Applied linear models: regression, analysis of variance, and experimental designs’’, 3rd ed. Homewood, Ill: Irwin, 1990; 38– 44, 62–104; 13. Pearson K. Mathematical contributions to the theory of evolution. III. ‚’’Regression, heredity and panmixia’’, Phil Trans R Soc Lond Series A 1896; 187:253–318; 14. Rodriguez RN. Correlation. In: Kotz S, Johnson NL, eds. ‚’’Encyclopedia of statistical sciences’’, New York, NY: Wiley, 1982; 193–204; 15. Seber GAF. ‚’’Linear regression analysis’’, New York, NY: Wiley, 1997; 48–51; 16. Solomon, D. C., Dragomirescu, S. E., (2009), – “New Dimensions In Enterprise’s Financial Performance Reporting: The Statement Of Comprehensive Income”, The Annals Of University Of Oradea Economic Science Series Tom Xviii; 17. Spearman C. ‚’’The proof and measurement of association between two things’’, Am J Psychol 1904; 15:72–101; 18. Thomas, R.L. – “Modern econometrics – an introduction”, Editura „Financial Times – Prentice Hall”, 1997; 19. http://scholarbank.nus.edu.sg/bitstream/handle/10635/14854/09Chapter%202. pdf?sequence=8 20. https://www.spl.harvard.edu/archive/splpre2007/pages/papers/zou/ REGRESSION.pdf 76 Romanian Statistical Review - Supplement nr. 1 / 2016 Risk Aversion and Individual Preferences Modelling Prof. Constantin ANGHELACHE PhD. Bucharest University of Economic Studies, “Artifex” University Bucharest Lecturer Mădălina Gabriela ANGHEL PhD. Assoc. prof. Aurelian DIACONU PhD. Artifex” University Bucharest Abstract Confronting risky situations is a feature of any decision making, in general, but, especially for specialized decision making contexts. Risk aversion concentrates the problems of modeling individual preferences. Models can grasp with accuracy and largely enough the fundamental human tendencies. As consequence, understanding economic behavior confronted to risk might be understood. Modeling market forecasting activities is possible based on “marginal consumer”. Thus, the vague use of mathematical instruments is necessary, but not enough as condition to develop an economic analysis. A historical approach of risk analysis might bring an idea regarding the development of the concept instruments used today when analyzing “risky choices” Key words: risk theory, diversification, risk aversion, risk premium, absolute risk aversion, relative risk aversion, utility function, marginal consumer, marginal utility, DARA, CARA, CRRA. A short presentation In 1738, Daniel Bernoulli published “Specimen theoriae novae de mensura sortis”, a paper in Latin, or “Exposition of a new theory on measurement of risk”. The English translation made in 1854 seemed to be non-technical and focused on the way two people confronted to the same risky situation might choose depending on psychological differences. While presenting his hypothesis, Bernoulli used three examples. In this sense, “Sankt Petersburg paradox” is quite famous and still present today in academic circles, and shows the importance of psychological aspects with regard to risky choices. Unfortunately, the celebrity of this paradox has overshadowed the other two examples. They show that the value of a risky situation is not equal to its mathematical expectation. One is known as “Sempronius” and it anticipates in an exemplary manner the contributions brought 230 years later in the risk theory by Arrow, Pratt et alias. Revista Română de Statistică - Supliment nr. 1 / 2016 77 Quoting Bernoulli, Sempronius owns goods at home worth a total of 4 000 ducats and in addition he possesses 8 000 ducats worth commodities in foreign countries from where they can be transported only by sea. Our common experience shows that from two ships one perishes.” Sempronius is confronted with a risk on his wealth. x̃ represents his wealth on a value of 4 000 ducats with probability (if ship is sunk), or 12 000 ducats with probability . We denote this lottery as x̃ being distributed as Its mathematical expectation is given by: (4000, Ex̃ 4000 + 12000 = 8000 ducats. Sempronius comes with a big idea. Instead of trusting all 8 000 ducats of goods to one ship, he “entrusts equal portions of his commodities to two ships”. Assuming that the ships follow independent but equally dangerous routes, Sempronius now faces a more diversified lottery ỹ distributed as (4000, ; 8000, ; 12000, ) In case Sempronius looses all his ships, he still has 4000 ducats wealth. AS the two risks are independent, their probability equals the result of the individual events, exactly ( )2 = .Thus, there is the probability of that the two ships to be back home, and thus the final wealth of Sempronius is 12000 ducats. Evidently, there is also the other chance for him to have only one ship back with the final result of half of the previous wealth. The final wealth would be of 8000 ducats. The probability of this event is of completing the other two events with probability of . The common experience leads to the idea that diversification is good, so that we can expect the value attached to the event ỹ to exceed the one attached to the event x̃ . Even so, calculating the expected profit, we may obtain: Eỹ = ¼ 4 000+ ½ 8 000+ ¼ 12 000 = 8 000 ducats, which means the same value as for Ex̃. Thus, diversification seems useless! Using intuition, Bernoulli’s demonstration will lead to the conclusion that lottery ỹ might bring a satisfaction value larger than lottery x̃, thus the satisfaction level generated by lottery ỹ will make Sempronius choose the first of them. Thus, Bernoulli shows that diversification generates a transfer conserving means to gain wealth, from extremes to the middle. Thus, transferring 78 Romanian Statistical Review - Supplement nr. 1 / 2016 probability from x=4 000 to x=8 000 a, increase of expected utility is obtained. Each transferred probability unit generates an increase of expected utility equal to u(8000) - u(4000). On the contrary, transferring a certain probability from x = 12 000 to x = 8 000, it will reduce the expected probability. Each transferred probability unit will generate a reduction of the expected utility equal to u (12 000) – u (8 000). But concavity of u implies that u (8 000) - u (4 000) u (12 000) - u (8 000), (1) With the positive aspect that these transfers combined should dominate the negative effect. Definition and characteristics of risk aversion in investments Supposing agents live only one investment cycle, investing their wealth purchasing and consuming goods and services. The final wealth is formed of the initial wealth w plus the outcome of any risk borne during the period. Definition 1: An agent has risk aversion if, at any level of wealth w he dislikes every risk with expected payoff of zero. Thus, he always prefers receiving the expected outcome of a risky decision with certainty, rather than the risk itself. For an expected-utility maximize with a utility function u, this implies that, for any risky situation z̃ and for any initial wealth w, E u ( w + z̃ ) u ( w + E z̃). (2) In Sempronius’ case, his risk aversion is obvious. This is true anytime the utility function is concave. If marginal utility decreases, the possible loss reduces more the utility generated by potential gain. The preference for diversification is intrinsically equivalent to risk aversion, at least under the model of expected utility of Bernoulli. Reversely, if u is convex, the inequality will be reversed. That is why the agent will prefer risky decision to any mathematical expectation revealing his inclination to taking risks. This behavior is known as loving risk behavior. Finally, u is linear, then the welfare Eu is linear in the expected payoff of lottery. If u(x) = a + b x for all x, then we have Eu ( w +z̃) = E [a + b (e + z̃ )] = a + b ( w + Ez̃) = u ( w + Ez̃). This implies that the agent would rank the lotteries according to their expected outcome. This behavior is called risk-neutral. A decision maker is risk averse if inequality (2) holds for all w and z̃ if and only if u is concave. This proposition is in fact a rewriting of the famous Jensen inequality with the conclusion that the decrease of the marginal utility means an increase of the income. Lately, many researchers consider that risk aversion is generated by marginal utility decrease. Other authors consider that there is no connection between the two concepts. Revista Română de Statistică - Supliment nr. 1 / 2016 79 Risk premium and the equivalent of certainty An agent is risk averse in case he dislikes zero-mean risks. The degree of risk aversion might be evaluated by asking him how much he is inclined to pay in order to avoid a zero-mean risk z̃. The answer to this question will be defined as risk premium associated to that risk. The agent will end up with the same wealth either he will accept the risk or he will pay the risk premium. When risk z̃ has a expectation other than zero, we usually use the concept of the certainty equivalent. This is the sure increase in wealth that has the same effect on welfare as having to bear the risk z̃. The cost of risk, as measured by the risk premium, is proportional to the variance of payoffs. Thus, the variance seems to be a good measure of the degree of riskiness of lottery. This is why many authors used mean-variance in order to model the behavior under risk. The validity of these models is accurate only when the risk is small. In such cases, mean-variance approach can be seen as a special case of the expected- utility theory. The mean-variance played a very important role in the development of the finance theory. The degree of risk aversion Considering the small risk only, we can see that agents with a larger absolute risk aversion are more reluctant to accept small risks. In their cases, the minimum expected payoff is larger. Technically, the measure of the degree of risk aversion is a measure of concavity of the utility function. It measures the speed at which marginal utility is decreasing. In small risk cases, we need to know to determine whether a risk is desirable is the degree of concavity of u locally at the current wealth level w. For larger risks, we need to know much more in order to make a decision, meaning needing to know the degree of concavity of u at all wealth levels. So, the degree of concavity must be increased at all levels of wealth to guarantee that a change in u makes the decision maker more reluctant to accept risks. Decreasing absolute risk aversion and prudence As we could see, risk aversion is driven by the fact that one’s marginal utility is decreasing with wealth. Arrow considers that wealthier people are generally less willing to pay in order to eliminate fixed risk. Thus, the risk premium associated to any risk is decreasing in wealth if and only if absolute risk aversion is decreasing; or if and only if prudence is uniformly larger than absolute risk aversion. Notice that Decreasing Absolute Risk Aversion (DARA), a very intuitive condition, requires the necessary condition that the utility function to be positive, or that marginal utility to be convex. 80 Romanian Statistical Review - Supplement nr. 1 / 2016 Relative risk aversion Absolute risk aversion is the rate of decay for marginal utility. More particularly, absolute risk aversion measures the rate at which marginal utility decreases when wealth is increased by one monetary unit. The index of absolute risk aversion is not unit free, as it is measured per monetary units. Economists prefer unit-free measurements of sensitivity. Defining the index of relative risk aversion R as a rate at which marginal utility decreases when wealth is increased by one per cent. In terms of standard economic theory, this measure is simply the wealth-elasticity of marginal utility. Finally, the measure of relative risk aversion is simply the product of wealth and absolute risk aversion. The relative risk premium is equal to half of the variance of the proportional risk times the index of relative risk aversion. This can be used to establish a range for acceptable degrees of risk aversion. There is no argument for or against decreasing relative risk aversion. Arrow considered that relative risk aversion is likely to be constant or perhaps increasing, but the intuition is not clear as was for decreasing absolute risk aversion. Under the intuitive DARA assumption, becoming wealthier also means becoming less risk-averse. This effect tends to reduce risk premium. On the other hand, becoming wealthier means facing larger absolute risks, that tends to raise risk premium. It is not clear if the first effect or the second will dominate, as well as there is no a priori reason to believe that the dominant effect will not change over various wealth levels. Some classical utility functions The expected utility (EU) theory is not accepted by everyone in economic theory. Some researchers consider EU criterion satisfying for those who find expected utility too restrictive. Economics and finance researchers consider EU theory as an acceptable paradigm for decision making under uncertainty. EU theory has a long “career” and a prominent place in the development of decision making under uncertainty. Even those who reject EU theory use it as a standard by which to compare alternative theories. Some researchers often restrict the EU criterion by considering a specific subset of utility functions in order to obtain solutions to different problems. Of course, particular utility functions have different implications. WE have to note that utility is unique only up to a linear transformation. We note that during the 1960s the theory of finance considered the subset of utility functions that are quadratic of the form u (w) = aw - w2, for w a. u should satisfy the requirement of not being decreasing, which is true only when w is smaller than a. Revista Română de Statistică - Supliment nr. 1 / 2016 81 Above wealth level a, marginal utility becomes negative. Since quadratic utility is decreasing in wealth for w a, many people might feel this is not appropriate as a utility function. However, it is important that we try to obtain models of human behavior with mathematical models. Quadratic utility functions exhibit increasing absolute risk aversion and this is why they are not in fashion anymore. The second set of classical functions is the so-called constant-absoluterisk-aversion (CARA) utility functions, which are exponential functions, whose domain is the real life. They exhibit constant absolute risk aversion with A (w) =a for all w. The fact that risk aversion is constant is often useful in analyzing choices among several alternatives. This assumption eliminates the income effect when dealing with decisions to be made about a risk whose size is invariant to changes in wealth. This is a reason for criticizing this function, since absolute risk aversion is constant than decreasing. Finally, the constant-relative risk-aversion (CRRA) class of preferences is a set of power utility functions which eliminate any income effects when making decisions about risk whose size is proportional to one’s level of wealth. The assumption that relative risk aversion is constant simplifies enormously many of the problems encountered in macroeconomics and finance. References 1. Bernoulli,D. (1954). Exposition of a new theory on the measurement of risk. (English Trans. by Louise Sommer.) Econometrica 22:23-36 2. Anghel M.G. (2013). Modele de gestiune şi analiză a portofoliilor, Editura Economică, Bucureşti 3. Anghelache C., Anghel M.G., Manole A. (2015). Modelare economică, financiarbancară şi informatică, Editura Artifex, Bucureşti, 290 pg., ISBN 978-606-8716-00-8 4. Anghelache C., Anghel M.G. (2015). Statistică. Teorie, concepte, indicatori şi studii ce caz, Editura Artifex, Bucureşti 5. Anghelache, C. (2008). Tratat de statistică teoretică şi economică, Editura Economică, Bucureşti 6. de Finetti, B. (1952). Sulla preferibilita. Giornale degli Economisti E Annali Di Economia 11 :685-709. 7. Nachman, D.C.(1982). Preservation of ‘more risk averse’ under expectation. Journal of Economic Theory, 28:361-368. 8. Pratt, J. (1964). Risk aversion in the small and in the large, Econometrica 32:122136. 9. Ross, S.A. (1981). Some stronger measures of risk aversion in the small and in the large with applications, Econometrica 3:621-638. 10. Segal, U. and A. Spivak, (1990). First order versus second order risk aversion, Journal of Economic Theory, 51:111-125. 11. Yaari, M.E. (1987). The dual Theory of Choice under Risk, Econometrica, 55:95115. 82 Romanian Statistical Review - Supplement nr. 1 / 2016 The major Macroeconomic Evolutions by the end of July 2015 Prof. Constantin ANGHELACHE PhD. Bucharest University of Economic Studies, Artifex” University of Bucharest Alexandru URSACHE PhD. Student Bucharest University of Economic Studies Abstract This paper describesthe main macroeconomic evolutions recorded at the level of the Romanian economy during the recent period. Considering the data published by the National Institute of Statistics, the authors present the results describing the status and dynamics of the economy of Romania. Key words: economy, evolution, outcome, politics, country In 2015, Romania tries to cope with the effects triggered by the economic-financial crisis, which either as develipong phenomenon or as a new wave, will be prolongued with effects in the next years. The presence of Romania within the European Union is underlining the fact that an adequate program of steps meant to secure a unitary framework is required in order to facilitate the implementation in practice of the postadhesion actions, mainly the absorption of the communitarian funds put at the disposal of Romania. But our country is holding a bi-factorial major problem, synthesized in programs and projects of substance and, mainly, in its incapacity of co-financing. Under the circumstances, we may recognize ourselves as contributing-country to the European Union. The outcomes recorded for 2010-2014 should be analyzed through the angle of the fact that the outburst of the financial and economic crisis, during the spring of the year 2007 in the USA and in 2008 in Europe also, including Romania as well within the last part of the year 2008, has generated a number of global and individual negative effects, of different intensity degree from country to country and, meantime, approaches in respect of over-climbing the crisis, both of global character and at national level. Coming later to us, or being too deeply psychologically stimulated, in 2009-2012, the crisis has produced economical-financial effects, beyond imagination. Revista Română de Statistică - Supliment nr. 1 / 2016 83 After this year, the concerns of the government aiming to rectify some of the austerity steps started to give outcomes despite the fact that they were altered by the lack of the required financial sources. The major macroeconomic evolutions The indicator the most synthetic for Romania, concerning the outcomes recorded in 2010, is given by the Gross Domestic Product which counted for 513,640.8 million of lei, expressed in the current prices of the year 2010. In 2011, the increase of GDP was some 1.1% as against 2010, which, adjusted at the level of 2011, reveals a value of 519,290.8 million lei. The year 2012 emphasized an increase of GDP by 0.6% as against 2011 and by 4.9% as against the same year, deflated data, reaching the level of 596,681.5 million lei, definitive data. In 2013, based on semi-definitive data, there is an annual increase by 3.5% of GDP as against 2012. In absolute figures, the value of GDP in 2013 counted for 617,565.4 million lei, non-deflated data. In 2014, the evolution of GDP kept on increasing, if considering the current provisional, reaching a level of 102.8% as against 2013. The macroeconomic results indicators have recorded a negative evolution over the period January 2009 - January 2012 as a result of the effects of the economic and financial extended crisis, the lack of efficiency of the government activity and lack of a coherent anti-crisis program, based on pro-active steps. The GDP recorded decreases of during the period 2010-2012. Meantime, the inflation target could not be hit, the direct foreign investment diminished. In the first year 2014, the foreign debt increased, the domestic public debt multiplied, the foreign payments balance is recording a cumulated deficit, the population income stalled, some national economy branches recorded stagnations, the consolidated budget became volatile due to reduced certain incomes as a result of an adverse or unconcerned collection etc. Since 2013, the situation recovered, as there is a better collection of incomes to the consolidated budget. GDP/capita, calculated on the basis of the purchasing power parity counted in 2013 for 10,759 units standard purchasing parity (the monetary unit of reference at the level of the European Union, as conventional currency which excludes the influences of the differences between the national prices). In 2014, this synthetic indicator reached the level of possible 11,060 purchasing units. Out of the data analysis we see, first of all, that for almost all the cases, the quarter to quarter evolution is a relatively positive one, both in respect of the comparison with the previous quarter and as against the corresponding quarter of the previous year, emphasizing an increase at the level of EU28. We notice that 7 countries recorded slight, insignificant increases. The rest of the countries, including Romania as well, are still facing in 2013 the effects of the crisis triggered by recession. In 2012, the growth 84 Romanian Statistical Review - Supplement nr. 1 / 2016 rhythms tempered, and make way for the interpretation of the crisis effects’ comeback, in the second wave. The problem of the Euro union, triggered by the situation in Greece, Italy, Spain, Portugal and Ireland, stirring many analyses that lead towards pessimistic perspectives. Growth rates of GDP (seasonally adjusted data) : Data not available. (1) Percentage change compared with the same quarter of the previous year calculated from non-seasonally adjusted data. (2) Percentage change compared with the same quarter of the previous year calculated from working-day adjusted data. (3) Growth rates are calculated using the trend component. (4) All growth rates based on ESA2010 methodology. Note: The seasonal adjustment does not include a working-day correction for Ireland, Portugal, Romania and Slovakia. Data source: Eurostat. If considering the European context, from the point of view of the recession phenomenon the situation is showing that there is only one country, i.e. Poland, managed to stay out of this condition, recording subsequent increases. Other 14 EU member countries, among which Germany (the engine of the European economy) with a noticeable increase in 2013-2014 too have recorded increases. Some of them, such as France, Holland, Slovakia, Denmark, have recorded consolidated increases during the two quarters, which forecasts a positive evolution expected at the level of the entire year 2014. Other countries, such as Belgium, Spain, Hungary, have marked Revista Română de Statistică - Supliment nr. 1 / 2016 85 labile comings out at least for one of the two quarters, which denotes a certain uncertainty for the end of 2013, the recession being any time possible. The situation occurring in this respect implies certain discussions which, briefly, may resume to the following basic aspects: • The imports decreased comparatively to the previous year. The imports kept on being profitable since, even if sometime an appreciation of the national currency against Euro and USD has been recorded, the wholesale and retail prices were not adjusted by cutting-off, the companies considering that they are a gained position which is not advisable to give up; • On the other side, the exports increased during the period 2012 - 2014 and the first six months of the year 2015, because this fluctuation of the exchange rate against the two currencies of the foreign exchange panel of reference did stimulate the domestic production for export, this becoming much more profitable at export, the situation will maintain on the same trend in the following period; • Normally, one country deficit, including the Romania one, is not alarming to the extent it makes part of a program of external loans, directed by projects, which do not harm the national wealth; • In the context of the imports propensity towards the consumer goods, the situation can be evaluated as a negative one. References 1. Anghel M.G. (2015). Analysis on the Indicators related to the structuring of the Monetary Mass in Romania after the adhesion to the European Union, Romanian Statistical Review - Supplement/No. 6, pg. 26 – 33 2. Anghel M.G. (2014). Econometric Model Applied in the Analysis of the Correlation between Some of the Macroeconomic Variables, Romanian Statistical Review Supplement/No. 1, pg. 88 – 94 3. Anghelache C. (2015). România 2015. Starea economică în continuă creştere, Editura Economică 4. Anghelache C., Manole A., Anghel M.G. (2015). Macroeconomic Evolutions in Romania by ohe End of The Year 2014, Romanian Statistical Review - Supplement, No. 2, pg. 46 – 54 5. Anghelache C., Anghelache G.V., Anghel M.G. (2015). The monetary evolution, placements and resources, Romanian Statistical Review – Supplement, No. 4, pp. 72-80 6. Anghelache C., Anghel M.G. (2014). Modelare economică. Concepte, teorie şi studii de caz, Editura Economică, Bucureşti 7. Anghelache C., Manole A., Anghel M.G. (2014). The business environment and the foreign investment, Romanian Statistical Review - Supplement/No. 10, pg. 7 – 14 8. *** www.insse.ro – official site of the National Institute of Statistics of Romania 86 Romanian Statistical Review - Supplement nr. 1 / 2016 The Gross Domestic Product evolution in Romania Assoc. prof. Alexandru MANOLE PhD. “Artifex” University of Bucharest Prof. Sebastian KOT, PhD Czestochowa University of Technology Marius POPOVICI PhD. Student Georgiana NIŢĂ PhD. Student Bucharest University of Economic Studies Abstract In this paper, the authors study the characteristics of the Romanian economy from the viewpoint of the Gross Domestic Product. The analysis focuses on the overall evolution of the indicator, especially during the interval 2015-July 2015, the modification by categories of resources and utilizations. Key words: GDP, utilizations, resources, interval, growth, value As to the GDP evolution comparatively with the corresponding periods of the year 2009, in the case of Romania it is resulting, first of all, that the decrease of -1.3 compared to 2009 was reasonable. In 2012, an increase of GDP by some 1.1% was recorded. GDP recorded in 2010 a value of 522,561.1 million lei, reaching 578,551.9 million lei in 2011, and 596,681.5 million lei, definitive, deflated data, in 2012. The GDP value in 2013 increased by 3.5%, reaching 617,565.4 million lei, in the context of the crisis which, on both internal and international plan, continued to affect the economic evolution. Meantime, in 2014, the GDP reached a value of 634,857.2 million lei while, by 30.06.2015, the GDP value counted for 351,281.2 million lei. Quarterly GDP evolution, over the period 2013-2015 1st quarter 2nd quarter 3rd quarter 4th quarter In % as against the corresponding period of the previous year 2013 102.1 101.4 104.2 105.2 Raw series 2014 104.1 101.5 103.0 102.7 2015 104.3 103.3 2013 101.9 102.0 104.0 104.7 Seasonally 2014 103.7 102.4 102.9 102.6 adjusted series 2015 103.8 103.7 In % as against the previous quarter 2013 101.1 101.5 100.8 101.2 Seasonally 2014 100.2 100.2 101.3 100.9 adjusted series 2015 101.4 100.1 - Year 103.4 102.8 - Data source: National Institute of Statistics, press release no. 249/07.10.2015. Revista Română de Statistică - Supliment nr. 1 / 2016 87 Over the period 2001-2008 GDP progressed in leaps, recording positive evolutions. Starting in 2009, under the influence of the economicfinancial crisis, the decrease of the economic growth triggered. The GDP evolution over the period 2001- 2015* (The corresponding period of the previous year = 100) *) provisional data, estimate for 2015 Data source: National Institute of Statistics, press release no. 249/07.10.2015. By comparing the Romania GDP increase level in 2012 with some other countries out of the European Union, we shall see that it counts as almost the lowest. The analysis will get a more significant outline if we follow the way in which the GDP developed in 2014 and the first two quarters of 2015. Thus, in Q1 2015 GDP grew by 4,3% as gross series (3,8% adjusted series) and in the second trimester grew by 3,8% gross series (3,4% adjusted series) as against the corresponding quarter of the previous year. 88 Romanian Statistical Review - Supplement nr. 1 / 2016 The GDP evolution – seasonally adjusted Data source: National Institute of Statistics, Press release no. 249/07.10.2015. For the first semester of 2015, the total volume of GDP, in current prices, counted 351,181.2 million lei. The GDP alteration factors by categories of resources In 2014, as well as during the first six months of 2015, the GDP has been achieved on the account of the activity carried out in the frame of the main branches of the national economy. The contribution differed from the point of view of the gross added value recorded at the level of each branch. The net taxes on product brought in the first semester of 2015 a positive contribution, representing some 12.9% out of the GDP, the constructions increased by 0.2%, while the industry recorded an increase of 0.2%. Also, in 2014 the contribution of the agriculture, forestry and fish breeding was reduced, and during the first six months of 2015 they represented 2,4% of the GDP. Revista Română de Statistică - Supliment nr. 1 / 2016 89 Contributions to the GDP evolution, by categories of resources 2015 / 2014 (first semester) Data source: National Institute of Statistics, Press release no. 249/07.10.2015. In 2015, for the first six months, the same trends persisted, with the mention that agriculture marked a slight recoil. For the first half of the year 2015, there is slight increase to be noted for the economy evolution. The agriculture kept on maintaining within normal parameters of influence, recording a slim evolution. Contribution of the main categories of resources to GDP increase during the first six months of 2015 (%) Indicator Sem. I Gross Domestic Product 3.4 Agriculture, forestry and fish breeding. -0,1 Industry, including energy 0,7 Constructions -0,2 Trade, cars and household appliances repairs; hotels and restaurants, telecommunications 1.1 Financial, real estate, renting and services to companies activities 0,2 Other services activities 0,1 Total gross added value 2.8 Net taxes on product 0,9 Data source: National Institute of Statistics, Press release no. 249/07.10.2015. 90 Romanian Statistical Review - Supplement nr. 1 / 2016 Relevant as regards the GDP forming by categories of resources (as alteration factors) is also the structural evolution during the period 2003-2015 which is described by the following table. Weight of the main categories of resources to the GDP forming (%) Indicator 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015* Agriculture, hunting, forestry, fishing and fish 11,6 breeding. Industry, including 24,7 energy Constructions 5,7 Trade, cars and household appliances repairs; hotels 20,3 and restaurants, telecommunications Financial, real estate, renting and services to companies activities 12,3 12,6 8,4 7,8 5,8 6,7 6,3 6,0 7,3 4,7 4,8 3,0 2.4 24,9 24,8 24,5 24,3 22,9 23,8 26,4 25,0 25,2 25,1 32,5 23.1 5,9 6,5 7,4 9,1 10,6 9,8 7,3 7,2 7,3 7,4 4,8 4.3 20,6 21,7 22,2 22,7 21,9 21,2 20,9 21,2 20,9 21,0 15,6 18.0 12,3 13,2 13,3 13,7 14,0 15,1 16,2 14,1 14,0 13,2 16,9 13.4 Other services activities 14,3 13,0 13,7 13,1 13,0 13,0 13,8 12,0 12,8 12,8 12,7 14,0 14.0 Net taxes on product 10,7 11,7 11,7 11,4 10,9 10,0 11,2 12,4 13,9 10,8 13,2 12.9 11,1 *) provisional, estimate data Data source: National Institute of Statistics, Press release no. 249/07.10.2015. The activities carried out by services, industry, constructions and the net taxes on product, together, brought in a decisive contribution to the GDP decrease, which means a negative feature for the Romanian economy which, although restructured gave up a number of industrial sub-branches committing itself on the way of developing the services production, constructions and so on, but failing to cope with the effects of the crisis, correlated also with the non-existence of an appropriate governing plan, set up at the beginning of the phenomenon. The weight of the main categories of resources in the formation of GDP in the first half of 2015 reveals that the industry keeps on remaining on the first place, having a slight trend of increase as against the corresponding period of the previous year. The GDP evolution by categories of utilizations From the point of view of the utilizations in the GDP forming during the year 2013, there have contributed: the stocks variation, the net export, the gross forming of fixed capital, the final collective consumption of the public administration, the final individual consumption of the households. Revista Română de Statistică - Supliment nr. 1 / 2016 91 When analyzing the data available for 2014, we have to consider as starting point the actual situation being recorded by our country during this year. Thus, for instance, the stocks variations recorded a lower contribution, while the net export, namely the difference between exports and imports, recorded a more reduced effect, following the reduction of the deficit of the foreign trade balance. Under such circumstances, we find out that, from the point of view of the utilizations, the GDP formation has been achieved by the contribution of the following factors: gross forming of the fixed capital, final individual consumption of population with a decrease of -0.4%, which implies the following conclusions: • From the point of view of utilizations, positive influences on the GDP achievement have been recorded by the final collective consumption of the public administration, stocks variation and net exports; • Negative influences on the GDP forming have been recorded by the by the final individual consumption of households, and the gross forming of fixed capital. The analysis of the influence factors of the GDP forming by categories of utilizations may be emphasized by the analysis of rhythm at which, the categories of utilizations considered for the GDP achievement have influenced this achievement in 2014 comparatively with 2013. The same tendency is stated out also for the first six months of the year 2015. Thus, the individual consumption of households and the collective consumption of the public administration, together, have been reduced. A more marked decrease, has been recorded by the net export. Another negative effect has been recorded by the rhythm of increasing of the gross forming of fixed capital. The weight of the main categories of utilizations in GDP Indicator Actual individual consumption of the households Actual collective consumption of the public administration Capital gross forming Stocks variations Net export Year 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015* 75.7 77.5 78.5 77.9 75.3 74.0 72.7 8.3 7.7 7.6 7.7 8.2 21.5 21.8 23.7 25.6 30.2 31.9 0.6 1.8 -0.3 0.9 0.8 -0.6 -7.6 -9,0 -10.2 -12.1 -13.9 -13.0 25.6 -0.6 -5.9 9.8 7.9 72.6 72.4 72.7 7.1 7.3 7.1 22.5 22.3 22.2 3.5 3.9 4.1 -5.7 -5.9 -5.7 73.7 74.0 72.2 6.1 8.8 7.2 20.0 19.0 21.4 3.5 1.1 -0.9 -3.3 -0.5 0.1 *) provisional estimated data Data source: National Institute of Statistics. Press release no. 249/07.10.2015. The GDP evolution during 2014 follows the line of going on the recovery road from the process of recession. During the first six months of the 92 Romanian Statistical Review - Supplement nr. 1 / 2016 year 2013, the “un-accounted” negative effects of the year 2010-2012 have been taken over and then being followed by a slight increase, maintained in 2013-2014 and during the first six months of the year 2015. Thus, the GDP has not yet reached the level recorded in 2009; most of the branches recorded negative contributions, which implies the entrance into a macroeconomic managerial mess; the structure by branches and utilizations has been negative. In 2012, GDP grew by 1.1% as against 2011 and follows an oscillatory course in 2013, recording, during the first six months of the year an increase of 1.8% as against the same period of the previous year. The growth kept on being maintained in 2014 and the first six months of the year 2015. The survey on the economic evolution, considering the modifications of the GDP in the European Union countries, emphasizes the extremely critical situation existing on the European and, at a larger extent, international plan. The achievement of the Gross Domestic Product by ownership forms Out of the performed analysis, it results that for the period 20092014, the private sector contributed with 72.4%-76.9% to the GDP forming. The weight of the private sector, still low, has been generated mainly by the gross added value in the agriculture. Such an influence is a normal one if to consider that the agriculture has to face negative natural conditions. If comparing the weight of the private sector in the GDP achievement with the figures recorded for the previous periods, we find out that this weight is superior to all the periods being analyzed as from the year 2000, even as from the year 1990, up to date. In 2010-2014, for which we are actually performing a complete analysis, we find that the weight of the private sector in the gross added value increased as for the constructions field. What is really important is the fact that the weight of the private sector in the achievement of the gross added value by branches of the national economy and, eventually, to the GDP forming, kept on maintaining at a high level. Revista Română de Statistică - Supliment nr. 1 / 2016 93 Gross Domestic Product weight of the private sector in 2001 – 2015 *) Semi-final data. **) Estimate data. Data source: National Institute of Statistics, Statistical Bulletin no. 7/2015. It is obvious that the privatization of other administrations or extending the privatization at the level of branches already privatized will have the targeted effect. Here we have to underline the fact that such an analysis is not always pertinent since there will be and remain sectors of activity absolutely important for the national economy for which the state must keep its attributes of sole owner. References 1. Anghel M.G. (2015). Monedă. Teorie şi studii de caz, Editura Artifex, Bucureşti 2. Anghelache C. (2015). România 2015. Starea economică în continuă creştere, Editura Economică 3. Anghelache C., Anghel M.G., Manole A. (2015). Modelare economică, financiarbancară şi informatică, Editura Artifex, Bucureşti 4. Anghelache C., Anghel M.G. (2015). GDP Analysis Methods through the Use of Statistical – Econometric Models, Economica, Volume 7, No. 1 (91), martie 2015, pg. 124 – 130 94 Romanian Statistical Review - Supplement nr. 1 / 2016 5. Anghelache C., Manole A., Anghel M.G. (2015). Analysis of final consumption and gross investment influence on GDP – multiple linear regression model, Theoretical and Applied Economics, No. 3/2015 (604), Autumn, pg 137-142 6. Anghelache C., Manole A., Anghel M.G. (2015). The analysis of the correlation between GDP, private and public consumption through multiple regression, Romanian Statistical Review - Supplement, No. 8, pg. 34 – 40 7. Anghelache C., Manole A., Anghel M.G. (2014). Analysis on the Gross Domestic Product Evolution, Romanian Statistical Review - Supplement/No. 4, pg. 7 – 15 8. Anghelache C., Anghel M.G., Sacală Cristina (2014). The Gross Domestic Product Evolution, Romanian Statistical Review - Supplement, No. 12, pg. 12 – 20, 9. *** www.insse.ro – official site of the National Institute of Statistics of Romania Revista Română de Statistică - Supliment nr. 1 / 2016 95 The Inflation (Consumer Prices) in the Romanian Economy Prof. Constantin ANGHELACHE PhD. Bucharest University of Economic Studies, “Artifex” University of Bucharest Georgiana NIŢĂ PhD. Student Alexandru BADIU PhD. Student Bucharest University of Economic Studies Abstract This paper describes the effects that the inflation phenomenon exerted on the Romanian economy during the recent period. The valuation of the inflation magnitude is based on the characteristics of consumer price evolution, measured through appropriate indices,on the overall recordset, but also on groups of products/goods and services. Key words: inflation, consumer prices, indices, income, evolution. An important element to consider when evaluating the economic evolution of a country over a period of time consists of the way the consumer prices developed, both on an overall basis and by groups of goods and services, as well as of the dual comparison with the planned, forecasted target and the outcomes of the previous year. In the context of the steady concern as regards the adjustment of the system of the income collecting, based on the unique quota of taxation, as well as bringing the Fiscal Code to the level of correlative terms, in line with the actual situation of the country, in 2010-2015 there are a number of events occurring and worth to be outlined. First of all, the discussions between the Romanian Government and the I.M.F., have been finalized and the installments out of the granted credit were allocated. Practically, all of them, over 20 billion euro, were integrally transferred in 2011. There have been a number of elements which the I.M.F., intransigent and willing to see a market economy in action, did not agree with. Thus, for instance, there have been many concerns in respect of how to convince the I.M.F. to agree with a higher deficit of GDP or to keep on accepting the situation of having certain subsidies at the level of the national economy. The second essential phenomenon of the years 2010-2015 is given by the divergent evolution between the consumer price index, as an overall and in structure, in comparison with the evolution and appreciation of the 96 Romanian Statistical Review - Supplement nr. 1 / 2016 national currency, the new leu, against the two currencies which are forming the foreign exchange basket, respectively Euro and USD. Since a couple of years, as a consequence of the policy run by the National Bank of Romania, which undertook the responsibility of targeting and fixing the inflation at certain levels, the foreign exchange evolution of the national currency followed a trajectory which, from a economic and financial point of view, proved to be a positive one but, meantime, generated a negative effect on the Romanian exports, or for those working abroad and those living in the country, being meantime non-conform with the actual economical situation of the country. On this back-ground, during the period 2010-2015, we register also time intervals of slight appreciation of the national currency, in contradiction with the increase of the inflation rate, both on an overall basis and in structure by goods and services. We can outline two contradictory evolutions which we could identify from this point of view. On the one side, the increase of the consumption propensity of the population and, hence, the imperative requirement for steps meant to stop this tendency. Thus, at a first stage, the interests for the population deposits have been reduced after which, in order to improve the attractiveness of saving, they have been increased again aiming a sole purpose, respectively tempering the population propensity to consumption. The austerity steps being taken have stopped, in a natural way, the population consumption with immediate effect on the economic growth and deterioration of the standard of life. The revival measures of salaries and pensions, but also other social attempts, did not succeeded to improve, upon expectations, the incomes and subsequently the quality of life. On the other hand, in its concern as to targeting the inflation, the National Bank aimed to implement and control, permanently, the evolution of the foreign exchange rate, consequently the position of the national currency against the two foreign currencies – euro and dollar. Another typical element is given by the steady concern of the Executive and, mainly, of the National Bank, to observe the goals declared as regards the inflation targeting. Despite all steps being taken targeting slipped out of an actual control, lining up outside the forecasts, from 2010 until June 2012. Revista Română de Statistică - Supliment nr. 1 / 2016 97 Price increase in July 2015 – Percent – Increase of consumption prices in July 2015, as against: June December July 2015 2014 2014 -0,2 -1.4 -1.7 -1.2 -7.1 -7.3 -0.5 2,1 1.3 0,1 1,3 2.2 Indicators Monthly average increase of consumption prices during the period 1.I- 31.VII. 1.I- 31.VII. 2015 2014 0,2 -0,2 0,0 -1.0 0,3 0,3 0,2 0,2 Total Foodstuff *) Non-foodstuff Services *) Including beverage. Data source: National Institute of Statistics, Statistical Bulletin no. 7/2015. After 2013, a reverse trend of inflation evolution begins, kept under control. Among the non-foodstuffs recording a high average increase there are the natural gas, the thermo energy, tobacco and cigarettes, electric energy, water – sewage – sanitation, hygiene and cosmetics, postal services, interurban transport. Consumption price indexes, 2001-2015 - December previous year= 100 135 -%130 125 130,3 117,8 120 115 110 105 114,1 109,3 108,6 104,9 106,6 109 107,9 104,7 104,2 105,33104,5 101,7 100,2 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 100 *) Provisional data. Data source: National Institute of Statistics, Statistical Bulletin no. 7/2015. A comparative survey on the annual average inflation in the EU member countries during the period 2010-2015 shows that, along with Hungary, Romania was recording a high level of the inflation annual average level. 98 Romanian Statistical Review - Supplement nr. 1 / 2016 The annual average rate of inflation at the EU level, in 2014* measured on the harmonized indices basis (IAPC) *) Provisional data Data source Eurostat. At this point, there are a lot of other comments to be done but for a synthetic picture of the consumer price index we are holding present analysis only. References 1. Anghel, M.G. (2015). The Inflation (Consumer Price) Evolution, Romanian Statistical Review - Supplement/No. 1, International Symposium „Programs for Romania’s Economic Recovery in the Horizon 2020 Perspective”, pg. 128 – 132 2. Anghel M.G. (2014). Econometric Model Applied in the Analysis of the Correlation between Some of the Macroeconomic Variables, Romanian Statistical Review, Supplement/No. 1 3. Anghelache C. (2015). România 2015. Starea economică în continuă creştere, Editura Economică 4. Anghelache, C. (2014) – „Romania 2014. Starea economică pe calea redresării”, Editura Economică, Bucureşti 5. Anghelache, C.; Gheorghe, M.; Voineagu, V. (2013). Metode şi modele de măsurare şi analiză a inflaţiei, Editura Economică, Bucureşti 6. Karanassou, M., Snower, D. (2007). Inflation Persistence and the Philips Curve Revisited, Kiel Institute for the World Economy in Kiel Working Papers series 7. www.insse.ro – official site of the National Institute of Statistics of Romania Revista Română de Statistică - Supliment nr. 1 / 2016 99 The main aspects regarding the dynamics of the Industrial Production Indices Prof. Constantin ANGHELACHE PhD. Bucharest University of Economic Studies, “Artifex” University of Bucharest Assoc. prof. Aurelian DIACONU PhD. “Artifex” University of Bucharest Cristina SACALĂ PhD. Student Bucharest University of Economic Studies Abstract In this paper, the authors valuate the industrial production indices, measured for the Romanian economy. The rhythms of increase in the industrial field diminished and were different so that as against the increase recorded by the manufacturing industry, the decreases recorded by the extractive industry and the electric and thermo-energy, gas and water sector should be underlined; however, there have been increases for certain categories, such as the industry of durable goods, recording an increase of the industry of capital goods with, the industry of current usage goods with increase. Key words: Industry, indice, production, manufacturing, branch In 2010-2015 the industrial production indices are reflecting a slight increase as comparatively to the similar periods of the previous year, being largely influenced by the restructuring of the extractive sector, as well as by the decrease recorded at the level of the lohn production, which generated a slower rhythm of development at the level of the manufacturing industry. The fact that these other activities or branches had small weights within the total industrial activity from our country is to be noted. However, there are several other branches which recorded diminished indices, such as: textile production, clothes, shoes and leather articles production, rubber and plastics products, production of building materials and other non-metallic minerals and production of equipments and machinery. The first three categories, i.e. textile production, clothes, shoes and leather articles carried out their activity in the form of lohn production and recorded a tempering rhythm which might generate effects during the periods to come as well. On an overall basis, the industry kept on remaining on a positive position, meaning that it recorded a slight increase of the contribution to the GDP achievement in 2014 and 2015. 100 Romanian Statistical Review - Supplement nr. 1 / 2016 The privatization process could lead within the forthcoming periods to new decreases, both for the rhythm and the volume of the production of certain branches, but also of the total contribution which the industry might bring to the GDP achievement, by the obtained gross added value. The labor productivity by an employee in the industrial field followed a slow increase trend over the period 2010-2015. In the first seven months of 2015, the resources of prime energy increased. The evolution of the industrial production represents the sectors which have positively marked the evolution process during the period 2009-2015. This characteristic is specific to the manufacturing industry which, by the over-helming weight hold in the total industry production generated the same trajectory to the entire industrial production. This is significant, despite the fact that the production of electric and thermo energy, is following a practically opposite trend while the trend of the extractive industry is recording a flat evolution. The data show a fluctuating evolution of the production volume, compared to previous periods. The decrease is stronger in the extractive industry as well, the manufacturing industry and electric and thermo energy, recording decreases. The decreases have been stronger at the level of the large industrial groups, structured upon the goods destination. Here we have to mention the marked decrease of the production of capital goods and by almost a quarter for the production of intermediary goods. The decreases recorded by the import and the export of intermediary goods are going to jeopardize this sector production which is already a confirmed fact by the recorded decreases. The biggest decreases being recorded during the first seven months of 2015, comparatively with the corresponding months of the previous year are shown up by the metallurgical industry A similar trend of a significant magnitude is stated out in the case of the production of auto-vehicles for road transportation from a relatively slight reduction in October to a marked decrease December. A similar trend of a significant magnitude is stated out in the case of the production of auto-vehicles for road transportation from a relatively slight reduction in October to a marked decrease December. In July 2015, the prices of the industrial production increased by 1.03% as comparatively to the corresponding month of the previous year, on an overall basis. The industrial production is one of the few sectors which are marking a certain recovery at the EU level and that of many of the EU member states. The evolution of the Romania industrial production is also included in this allegation. Revista Română de Statistică - Supliment nr. 1 / 2016 101 Certainly, the positive evolution at the EU level is marked by the evolution of the industrial production in Germany, France, Italy and other countries, among which Ireland, Hungary, Denmark, Holland, with a relatively smaller weight. For 2014 and 2015, Romania recorded increases of the industrial production computed as seasonally adjusted series, atrend which was maintained in the first seven months of 2015 also. If considering the distribution by large groups, the increase of the industrial production in Romania has been more marked for the group of the capital goods industry and significantly lower for the group of the current usage goods. From the point of view of the industrial production increase recorded by Romania in 2013 and 2014, it can be seen that the production has been significantly higher as against the corresponding period of the previous year. During the first seven months of 2015, overall, industry recorded a growth by 3.3%. It is worthy to note that the increase recorded by Romania in September 2010 is higher comparatively with all the other European states which, most of them, excepting Poland, Slovenia and Holland, recorded decreases, in same cases quite significant. The situation keeps on being more or less the same in January 2011, comparatively with 2010 when the increase recorded by Romania is exceeded by Poland and Czech Republic only. In 2012, 2013 and 2014 the industry followed a slightly growing trend. However, these increases by groups have been counter-weighted the decreases recorded by the groups of current usage goods and durable goods. The indices of the industrial production, in 2010-2014 comparatively with 2009 and 2008, are showing, both on the overall and for the extractive and manufacturing industries branches, for all quarters as far as the first one is concerned. The indices for the first half of 2015 have manifested increases in all months. The manufacturing industry, electric energy, gas and water, as major sections, on one side and the capital goods industry, the intermediary goods industry and the power industry on the other side, with important increases are responsible for the mentioned increases at the industry level. The industrial production indices, as adjusted series, are also indicating a positive trend, although is circumscribed within increase levels relatively modest as to the overall industry, but nevertheless significant for the subbranch electric and thermo energy, gas and water and, respectively, the capital goods industry. The value indices of the new orders marked decreases in 2009, 2010, 2011 and 2012, and during the period 2013-2015 the trend reversed. In 2014 102 Romanian Statistical Review - Supplement nr. 1 / 2016 comparatively with 2013, the situation changed in respect of the new orders within the manufacturing industry from Romania, mainly in the situation of the manufacturing industry working on orders basis. Significant increases have been recorded in the case of orders value index for the manufacturing of chemical substances and products group, metallurgical industry, and manufacturing of road conveyance auto-vehicles, out of which absolutely remarkable was the increase recorded for the external market, as a consequence of the well known evolution of the Dacia cars exports to west-European countries, mainly to Germany. The actual supplies of goods established on the basis of the turnover indices are reflecting an uncertainty tendency as regards the producers’ capability to capitalize the achieved production, on one hand and the payment difficulties of the buyers, on the other hand. The labor productivity increased yearly over the period 2002 – July 2015, simultaneously with the decrease of the occupied population, so that during the first seven months of 2015, the productivity of labor grew by 2.7% (provisional data) per total industry. References 1. Anghelache C. (2015). România 2015. Starea economică în continuă creştere, Editura Economică 2. Anghelache C., Anghel M.G. (2014). Modelare economică. Concepte, teorie şi studii de caz, Editura Economică, Bucureşti 3. Anghelache, C. (coord.) (2014). Statistical-Econometric Models Used To Study The Macroeconomic Correlations, Romanian Statistical Review-Supplement, December 2014 4. Anghelache C., Mitruţ C., Voineagu V. (2013). Statistică macroeconomică: Sistemul Conturilor Naţionale, Editura Economică, Bucureşti 5. Anghelache, C. (2009). Indicatori macroeconomici utilizaţi în comparabilitatea internaţională, Conferinţa a 57-a „Statistica – trecut, prezent şi viitor”, ISBN 97890-73592-29-2, Durban, articol cotat ISI 6. Anghelache C. (2008). Tratat de statistică teoretică şi economică, Editura Economică, Bucureşti 7. *** www.insse.ro – official site of the National Institute of Statistics of Romania Revista Română de Statistică - Supliment nr. 1 / 2016 103 Production of Services during the Last Year Prof. Constantin ANGHELACHE PhD. Bucharest University of Economic Studies, “Artifex” University of Bucharest Prof. Radu Titus MARINESCU PhD. Assoc. prof. Aurelian DIACONU PhD. “Artifex” University of Bucharest Abstract This paper is dedicated to the analysis of services delivered for the population, as reflected in the data relevant for the Romanian economy. Among the aspects analyzed, there can be observed: the weight of services in GDP, the structure of service indicators, electronic commerce and sales by correspondence, tourism. Key words: production, services, influences, sales, tourism Comparatively to the previous year (2008), during the period 20092015, the indices of the market services supplied to the population, as well as the indices of the retail trade have recorded a decrease. This decrease is generated by the domestic demand (an element of the GDP utilization), which recorded an underlined decrease generated by the income reduction. Meantime, the services reached a signifncant weight in the GDP, out of which the retail trade only recorded a slow rhythm. The services contribution to the GDP achievement by the gross added value achieved in the frame of this sector means a positive development which, at this stage of the integration, means a lot for Romania. Out of the analysis of the structure of the carried out services activities, we note the fact that the retail trade recorded diminished rhythm of increase. Although the activity of sales by correspondence and virtual shops get developed and permanent, this type of trade recorded decrease of 2.9% as against the year 2009. But, as from 2012 on, the services production showed a significant increase. The dynamics of the services carried out to the population has been supported mainly by the activity of hotels and restaurants, which recorded no increase. Briefly, 2014 is a significant year as far as the production of services is concerned, by the following major guide marks: 104 Romanian Statistical Review - Supplement nr. 1 / 2016 • The increase of the weight services hold as for the GDP achievement; • The structural balancing of the services carried out to the population; • Differentiated decreases, for certain fields as already mentioned, quite significant, of the production of services spread on various zones, reflecting in fact the cynical effects of the economic crisis; • The employment of a large number of persons in activities of carried out services, which tendency should mark a similar evolution during the forthcoming period; • Maintaining the quality of the services carried out to the population; • Diminishing of the hotel activity; • Exceeding a high weight of the contribution which the production of services brings to the GDP achievement; • There has been a development of the financial and banking services as well as of capital market service, including thus a series of employees. Another aspect concerning the analysis in the field of the production of services in our country is given by the volume of the turnover figure achieved by the wholesale and retail trade of auto-vehicles, the retail trade with fuel etc. As comparatively the year 2010 in 2011 this field of activity recorded a significant decrease, continued in 2012. Here we have another element which denoted a negative evolution of the activity run in the field of the production of services in our country. Generally speaking, the turnover figure decreased for all sectors of activity, as a consequence of the alarming cut off of the population income. In 2013, a slight up warding trend is noticed, which becomes more stressed during 2014 and the first six months of 2015. As for the international tourism, the period 2010-2015 shows that the number of foreign visitors coming in Romania was low as against the touristic capacity of the country. Basically, the persons having friendship or kinship connections in Romania kept on visiting them. In this respect, the most numerous visits have been paid by citizen from Germany, United States, Israel, France, Republic of Moldova as well as from other countries where there is a significant number of Romanian natives. During the year we are analyzing, the departures of the Romanian visitors abroad decreased as comparatively with 2009. The negative rhythm of departures has accentuated in 2011, 2012 and 2013, with a slight increasing course in 2014 and 2015. Revista Română de Statistică - Supliment nr. 1 / 2016 105 In 2014 and 2015 we notice a positive trend in this field as well. Comparatively with the corresponding month of the previous year, in July 2015 both the arrivals and benighted to the structures of touristic accommodation functions recorded increases of 24.8%, respectively 20.8%. In comparison with the month of July 2014, in July 2015 the border checking points recorded increases for both the arrivals of the foreign visitors, by 13.3% and the departures abroad of the Romanian visitors, by 14.8%. The arrivals registered in the structures of touristic reception for July 2015 amounted 1281.3 thousand, meaning an increase of 24.8% as against those of July 2014. Out of the total number of arrivals, the Romanian tourists in the structures of touristic reception with accommodation functions represented in July 2015 79.8%, while the foreign tourists represented 20.2%, these weights being close to the ones recorded for July 2014. As for the arrivals of foreign tourists in the structures of touristic reception, the highest weight has been held by those coming from France (74.4% of the total of foreign tourists), while the tourists coming from European Union countries held 86.3% of the total. The benighted records in the structures of touristic reception for July 2015 counted for 3606.4 thousand, evidencing n increase of 20.8% as comparatively with the month of July 2014. Out of the total number of benighted those of the Romanian tourists arrived in the structures of touristic reception with accommodation functions counted for 85.5% in July 2015, while the benighted of the foreign tourists counted for 14.4% for the same period. As far as the benighted of the foreign tourists in the structures of touristic reception, the bigger weight went to those coming from Europe (73.7% of the total foreign tourists) and out of these ones 83.7% were coming from European Union countries. The average duration of the sojourn in July 2015 was of 3.0 days for the Romanian tourists and 2.0 days for the foreign tourists. The index of the net utilization of the accommodation places in July 2015 counted for 38.8% of the total structures of touristic accommodation, increasing by 4.1 percentage points as against the month of July 2014. Higher indices of utilization for the accommodation places in July 2015 have been recorded by hotels (48.6%), touristic villas (32.1%), pupils and children under school age camps (31.4%), touristic halting places (31.0%), hostels (29.6%) and holyday villages (29.0%). The arrivals of the foreign tourists in Romania, registered with the border points, in July 2015, counted for 1143.2 thousand, with an increase of 13.3% as against July 2014. 106 Romanian Statistical Review - Supplement nr. 1 / 2016 The majority of the foreign visitors originate in countries placed in Europe (92.6%). Out of the total arrivals of the foreign tourists in Romania, 55.3% originate in the European Union countries. Out of the European Union states, the most arrivals have been registered from Hungary (28.6%), Bulgaria (24.1%), Poland (11.7%), Germany (7.9%), Italy (4.9%) and Austria (3.8%). The departures of the Romanian visitors abroad, registered at the border points, in July 2015, counted for 1363.1 thousand, increasing by 14.8% comparatively with July 2014. The road conveyance means have been the most utilized for departures abroad, representing 81.0% of the total departures. The arrivals registered in the structures of touristic reception during the period 1.01-31.07.2015 amounted 5308.9 thousand, increasing by 16.0% as against those of the period 1.01-31.07.2014. Out of the total number of arrivals, the arrivals of the Romanian tourists in the structures of touristic reception with accommodation functions represented, during the period 1.01-31.07.2015, 76.7%, while the foreign tourists represented 23.2%, these weights being close to the ones of the period 1.01-31.07.2014. As far as the arrivals of the foreign tourists in the structures of touristic reception, the bigger weight has been held by those originating in Europe (74.8% of the total foreign tourists), out of which 85.2% were coming from European Union countries. Arrivals and benighted in structures of touristic reception with accommodation functions – period 1.01 – 31.07.2015 Arrivals Benighted Period 1.01Period 1.0131.07.2015 31.07.2015 Period 1.01- Period 1.01Period 1.01- Period 1.01as against as against 31.07.2014 31.07.2015 31.07.2014 31.07.2015 Period 1.01Period 1.01-thousand- -thousand-thousand- -thousand31.07.2014 31.07.2014 (%) (%) Total 4540.5 5305.9 116.9 10562.2 12073.2 116.2 Romanian tourists 3491.8 4079.3 116.7 8456.3 9512.1 115.6 Foreign tourists 1048.7 1085.6 117.8 2075.8 2461.1 118.6 off which: -Europe 815.2 924.1 113.4 1980.8 1792.3 113.4 - European Union 696.1 787.6 113.1 1328.8 1496.0 112.6 - Asia 118.1 178.9 147.2 286.0 363.6 142.0 -North America 73.6 85.0 115.5 135.9 168.4 123.9 -South America 8.6 9.2 107.0 17.9 19.0 106.1 - Africa 10.2 9.7 95.1 43.2 59.7 138.2 Data source: National Insitute of Statistics, Press release 249/07.10.2015. The benighted registered with the structures of touristic reception Revista Română de Statistică - Supliment nr. 1 / 2016 107 during the period 1.01-31.07.2015, counted for 12273.2 thousand, increasing by 16.2% as against those registered for the period 1.01-31.07.2014. Out of the total number of benighted, the benighted of the Romanian tourists in the structures of touristic reception with accommodation functions represented, during the period 1.01-31.07.2015, 79.9% while the foreign tourists benighted represented 20.1%. As far as the benighted of the foreign tourists in the structures of touristic reception, the bigger weight has been held by those originating in Europe (72.8% of the total foreign tourists), out of which 83.5% were coming from European Union countries. The average duration of the sojourn during the period 1.01-31.07.2015 was of 2.4 days for the Romanian tourists and 2.0 days for the foreign tourists. The index of the net utilization of the accommodation places during the period 1.01-31.07.2015 counted for 26.8% of the total structures of touristic accommodation, increasing by 2.8 percentage points as against the during the period 1.01-31.07.2014. Higher indices of utilization for the accommodation places during the period 1.01-31.07.2015 have been recorded by hotels (33.6%), accommodation facilities on ships board (22.6%), touristic villas (20.1%) and hostels (19.6%). The most arrivals of foreign tourists accommodated in the structures of touristic reception with accommodation functions* originated in Germany (142.1 thousand), Italy (115.8 thousand), Israel (114.7 thousand), France (76.8 thousand) USA 972.9 thousand) The arrivals of the foreign tourists in Romania, registered with the border points, during the period 1.01-31.07.2015, counted for 5077.7 thousand, with an increase of 10.8% as against during the period 1.0131.07.2014. The majority of the foreign visitors originate in countries placed in Europe (92.8%). Out of the total arrivals of the foreign tourists in Romania, 57.4% originate in the European Union countries. Out of the European Union states, the most arrivals have been registered from Hungary (31.9%), Bulgaria (26.9%), Germany (8.3%), Poland (6.7%), Italy (6.0%) and Austria (3.5%). The departures of the Romanian visitors abroad, registered at the border points, during the period 1.01-31.07.2015, counted for 7598.9 thousand, increasing by 11.7% comparatively with the period 1.01-31.07.2014. The road conveyance means have been the most utilized for departures abroad, representing 78.3% of the total number of departures. References 1. Anghel M.G. (2014). Evoluţii în domeniul construcţiilor şi transporturilor, ART 108 Romanian Statistical Review - Supplement nr. 1 / 2016 ECO - Review of Economic Studies and Research, Vol. 5/No. 1, pg. 54-62, ISSN 2069 – 4024 2. Anghelache C. (2015). România 2015. Starea economică în continuă creştere, Editura Economică 3. Anghelache, C. (2014). Romania 2014. Starea economică pe calea redresării, Editura Economică, Bucureşti 4. Anghelache C., Anghel M.G. (2014). Serviciile turistice în România, ART ECO - Review of Economic Studies and Research, Vol. 5/No. 2, pg. 90-95, ISSN 2069 – 4024 5. Anghelache, C. (coord.) (2014). Statistical-Econometric Models Used To Study The Macroeconomic Correlations, Romanian Statistical Review-Supplement, December 2014 6. Anghelache C., Anghel M.G., Bardaşu G., Popovici M. (2014). Evoluţia serviciilor turistice în România, ART ECO - Review of Economic Studies and Research, Vol. 5/No. 4, pg. 176-191 7. Anghelache C., Fetcu A.E., Anghel M.G. (2012). Considerations Regarding the Evolution of Tourism in the Last Decade, Revista Română de Statistică – Supliment Trim II, pg. 265 – 270 8.*** www.insse.ro – official site of the National Institute of Statistics of Romania Revista Română de Statistică - Supliment nr. 1 / 2016 109 Financial Inclusion, Focus on Romanian Migrants and their Families Prof. Constantin ANGHELACHE PhD. Olivia Georgiana NITA PhD. Student Alexandru BADIU PhD. Student Bucharest University of Economic Studies Abstract This paper aims to analyze financial inclusion of a particular segment that struggles most: the unbanked migrants, focusing on Romanian migrants and their families. It’s a common understanding that financial inclusion plays an important role in poverty reduction, economic development and growth, job creation, innovation and infrastructure. Banks have shown themselves unable or unwilling to provide relevant services to this particular segment of people and are acting to push this segment away from financial inclusion, indirectly condemn them to long-term poverty through financial exclusion. Keywords: financial inclusion, unbanked people, Romanian migrants, migrants ‘families, banks, poverty, rural areas Introduction Financial exclusion was defined as “a process whereby people encounter difficulties accessing and/or using financial services and products in the mainstream market that are appropriate to their needs and enable them to lead a normal social life in the society in which they belong”.1 This article is part of a wider research on migrants and their needs with a focus on Romanian migrants and their families. First part of the article presents studies about unbanked people at global level, financial services available for them and solutions to financial exclusion. The second part of the article concentrates on Romania, the country with the highest degree of unbanked people from Europe. 1. L. Anderloni, E. Carluccio, “Access to Bank Accounts and Payment Services”, in Anderloni L., Carluccio E. e Braga M., New Frontiers in Banking Services: Emerging Needs and Tailored Products for Untapped Markets, Berlino, Springer Verlag, 2006. 110 Romanian Statistical Review - Supplement nr. 1 / 2016 Financial inclusion – global approach According to World Bank the unbanked population in 2014 was 2 billion people – nearly one in three. The 2014 Global Findex database of the World Bank shows that, globally, 38% of adults remain unbanked for a range of psychological, cultural and educational reasons – but also due to the lack of interest in this segment from traditional financial institutions (banks). An absence of financial education, lack of awareness about financial services, unaffordable products, high transaction costs, paper work involved, travel distances, post-2009 distrust in the banking system – all these factors contribute to financial exclusion. As a result, 20% of the unbanked adults (over 400 million people) receive wages in cash and another 23% (440 million people) receive payments for agricultural products in cash. In Europe, according to European Commission, 30 million Europeans above 18 do not have a bank account - roughly 7% of all EU consumers. Among these unbanked adults, 6.4 millions are actually restricted or afraid to ask for a bank account. The situation varies greatly across the EU, with many Western and Northern EU countries showcasing over 90% account holding among the adult population, while at the other extreme Romania has the highest percentage of unbanked population. Romania stands out with the highest percentage (39%1) of unbanked population. In Romania, banks have approximately 5 million customers, representing 61% of the country’s active population. According to the National Bank of Romania, Romanians “own” 14.5 million bank cards, but just 11 million are actively used. The number of cards increased when people started to receive their wages in current accounts, not in cash. According to Global Findex Database, only 61% of Romanians aged over 15 years have a bank account, of which 46% have a debit card and 12% a credit card, again Romania ranks last (Figure1) 1. World Bank, Global Findex (http://www.bancherul.ro/romania-este-codasa-europei-si-lagradul-de-bancarizare-avem-cele-mai-putine-conturi-bancare-si-carduri-de-debit;-poatepentru-ca-sunt-prea-scumpe--14972) Revista Română de Statistică - Supliment nr. 1 / 2016 111 Percentage of Romania population over 15 years Figure 1 (Source: World Data Bank (http://databank.worldbank.org/data/home.aspx) In developed countries from Eurozone almost all adults have a bank account (95% of respondents aged over 15 years), while 81% have a debit card and 42% a credit card. Migrants also prefer cash, for several reasons: status, they need to proof their identity identify in order to open a bank account, and because they migrate apparently just for a few years, need just to send money to their families for financial support and some of them are not legally registered in the foreign country, the demand for banking account is low. language, represents a barrier for migrants in adapting abroad and request financial services (bank documents that should be filled in foreign countries, talking with tellers in bank branches) and also the lack of experience in using financial services in origin countries, which makes migrants to be reluctant on banks’ services. remittances, cash transfers from migrants to their families in origin countries. According to the study “Finacial inclusion and new means of payment” conducted by European foundation for Financial Inclusion in May 2013, migrants could use modern payment instruments but because of poverty, low income and unstable employment they are forced to mange with cash. Migrants have problems to store money securely and that’s why they usually carry cash. The key industry drivers of financial inclusion growth are technology and mobile money. Technology is playing a pivotal role in the process of 112 Romanian Statistical Review - Supplement nr. 1 / 2016 increasing financial inclusion by reducing the cost of delivery and increasing bank’s efficiency and productivity. Options to improve financial inclusion Innovative technologies and attractive financial services to meet unbanked people needs; Increase financial literacy; Extension and better affordability of traditional financial services, including greater access to bank branches and lending; Mobile money: facilitating remittances or payments; Remittances contribute to economic and human development, remittances increase household income and demand on financial services, by making recipients more inclined to join the formal financial sector. Romania - case study on unbanked people In a 2012 report1, Romania was identified as the country with the lowest degree of unbanked population (Figure 2). At that time, 56% of the Romanian population was banked, well below the Central and Eastern Europe average, which was 80%. Bank population in Central and Eastern Europe Figure2 Average 80% A special case is represented by the population from rural areas (according to Romanian National Institute of Statistics, 46% of Romanian population live in rural areas), where banks have fewer banking outlets (branches, ATMs) and people consequently don’t have access to the financial services offered by banks. The financial crisis in 2009 worsened the situation, 1 GFK Survey 2012, http://www.nocash.info.ro/desi-56-dintre-romani-au-relatie-cu-cel-putino-banca-romania-este-cea-mai-slab-bancarizata-tara-din-centrul-si-estul-europei/ Revista Română de Statistică - Supliment nr. 1 / 2016 113 as more bank branches closed. Where banks have kept branches open in rural areas, the costs of banking services are perceived by many in the unbanked population as being too high. In Romania, large differences in wealth, opportunity, education, skills, health are seen in many areas, and in the last decade they have intensified, particularly in rural ones. Nearly 40.2% of Romanian population in 2014 was exposed to the risk of poverty and social exclusion, given that, Romania occupied last place in EU, even if at EU level 122 million people, 24.4% of the European population were in this situation, according to data released by Eurostat, Oct 2015.1 In rural areas, incomes are relatively low compared to urban areas (for the year 20 11-503 euro / rural household compared to 621 euro / urban household). Revenue ratio represents 42% of total gross income/household in rural areas, while salaries are around 26%, according to 2011 Romanian Census, National Institute of Statistics. Rural areas are affected by the lack or deficiency of infrastructure, which has a negative impact on economic development and quality of life. County and communal roads have a length of 67,298 km (10.6% of national infrastructure modernized) of which 48% are paved and 29% of land (often impassable in the rainy periods).2 Although the length of water distribution networks and sewerage increased, the access to them remains low, only 13.6% of rural settlements were connected to the water supply in the year 2012.3 With its large unbanked population, Romania presents the most effective case study. The country ranks last in Europe regarding the usage rate of mobile banking, less than a fifth of Romanians using mobile devices to make payments, while the European average is close to 40%. The trend is toward more banked people – but slowly. On the other hand, Romania is the origination point of one of the largest migration flow in the EU – the “inbound” (from abroad to Romania) remittance market accounts for over 92% of the total remittance market; the outbound market, from Romania top other countries is comparatively insignificant at 8%. 2015 statistics indicate that an estimated 5 million Romanian migrants4 work abroad. Of these, 68%5 remit money to their families in 1. http://www.realitatea.net/40prc-dintre-romani-expu-i-riscului-de-saracie-i-excluderesociala_1811392.html 2. Transport routes, National Institute of Statistics 2011 3. Utilities activities of local interest, National Institute of Statistics 2011 4. The Profit Foundation, 2015, http://www.masapresei.ro/cinci-milioane-de-romani-sunt-plecati-in-strainatate-unde-traiesc-si-muncesc-acestia/ 5. According to 2015 TR Ltd surveys in main countries of remittance to Romania: Italy, Germany and Spain 114 Romanian Statistical Review - Supplement nr. 1 / 2016 Romania – leading to an estimated 3.4 million Romanians sending money to their families in Romania. According to National Bank of Romania 2013 statistics, Romanians in Italy have sent to their families in the home country a total amount of €925 million, followed by Romanians in Germany with €595 million, USA €460 million and Spain €393 million. According to Transfer Rapid research, each sender remits money to roughly 1.525 beneficiaries, meaning that 5.2 million people in Romania receive money from remittances. Based on recent World Bank Global Findex Database, 39% of the 5.2 million people receiving remittances are unbanked Per World Bank statistics, Romania has been among the top inbound remittance markets in the EU, with an average €6 billion annual remittances sent over the past 10 years. 1 In 2013, Romania was on the third position in the top of ECA countries at receiving remittances with $3.6 billion, after Ukraine with $9.3 billion and Tajikistan with $4.1 billion. 2 According to the National Bank of Romania, remittances from Romanians working abroad in the first nine months of 2015 reached 3.3 billion EUR, slight increase of 148 million EUR over the same period in 2014 and with 68 million more than in January- September of 2013.3 (Figure3) Remittances from Romanian working abroad (Source: National Bank of Romania and) Figure 3 1. World Bank, Migration and Remittance Flows: Recent Trends and Outlook, 2013-2016 2. World Bank, Migration and Remittance Flows: Recent Trends and Outlook, 2013-2016 3. http://www.bancherul.ro/romanii-care-muncesc-in-strainatate-au-trimis-acasa-3,3-miliarde-euro-in-primele-noua-luni-din-2015,-mai-multi-decat-anul-trecut--15191 Revista Română de Statistică - Supliment nr. 1 / 2016 115 The identified problem on the market is that banks are not able to offer appropriate services to a large segment of the population, leading to significant number of unbanked individuals. While banks have shown themselves unable or unwilling to provide relevant services to this particular segment of people (usually poorer and primarily in rural areas), at the same time banks are not pleased with lots of unbanked people coming into their branches just to make cash withdrawals over the counter – and are acting to push this segment away from financial inclusion, indirectly condemning them to long-term poverty through financial exclusion. However, according to National Bank of Romania, Romanians prefer cash, not saving money and all their cash goes into consumption. Romanian Banks’ concern is in finding a way to address all the 19 million Romanians to enter into a commercial relationship with a bank. “Banks are trying to rebuild trust in the customer relationship, which has been hit hard in recent years due to financial crisis” declared Radu Gratian Ghetea, President of the Romanian Association of Banks. Even if mobile money was another solution for increasing financial inclusion, due to the presented data, Romania is not the case, being the last country in Europe, by the financial inclusion degree and usage rate of mobile banking. Banks perceive the unbanked individuals receiving remittances at their teller windows as bottlenecks to effective banking activity as these individuals generally receive small amounts of money while occupying teller’s time (an estimated 9 to 10 minutes of teller time for each unbanked remittance service). In Romania, the company Transfer Rapid is known for developing innovative technological services and products for Romanian migrants and their families, now launching an innovative solution for unbanked migrant’s families in the home country. This service, entitled TR3A (“Anyone, Anywhere, Anytime”), seeks to provide access of unbanked individuals to financial infrastructure, acting towards financial inclusion and a higher quality of life for a vulnerable segment of society. This solution symbolizes the connection bridge of these two apparently irreconcilable market segments: banks and the unbanked population, by offering an innovative cash withdrawal service from ATMs, without the need of a credit card or a bank account. Once adopted, the solution will offer a unique way for unbanked people to access the bank networks, providing a valuable service while rebuilding trust and touch-points between banks and the unbanked population – the solution seeks to meet an increasing need of both the unbanked population in 116 Romanian Statistical Review - Supplement nr. 1 / 2016 Romania receiving remittances from abroad, as well as the need of banks to reduce servicing costs at the teller window in the case of unbanked individuals with perceived low Life Time Value (LTV) to the bank. Conclusions Financial inclusion of unbanked individuals into the financial system via alternative, more cost-effective channels than those traditionally offered by banks represents a long-term interest of all countries. This article showed a proportion of the vulnerable unbanked segment, represented by migrant families, but of course solutions have to be extended to migrants working abroad and to all unbanked people. Migrants and remittances should be favored in order to increase development impact and financial inclusion. And for this to be possible a series of actions should be held: financial education for migrants and their families in origin countries, financial services and products customized on their needs, innovative technologies in term of access to financial services, consumer protection and cost reduction. In the same time, banks should be open to new technologies provided to attract this particular segment of population: unbanked people; improve tellers’ attitude towards unbanked individuals when introduce services different from the basics ones. References 1. Anghelache, C. (2015) – “România 2015. Starea economică în continua creştere”, Editura Economică, Bucureşti 2. Anghelache, C. (2008) – “Tratat de statistică teoretică şi economică”, Editura Economică, Bucureşti 3. The World Bank, the Global Findex Database 2014, “Measuring Financial Inclusion around the World”, by Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, Peter Van Oudheusden, April 2015 4. The World Bank, “Migration and Remittance Flows: Recent Trends and Outlook, 2013-2016”, October 2013 5. The World Bank, “Financial Inclusion Strategies Reference Framework”, June 2012 6. IMF Working Paper, Statistics Department “Assessing Countries’ Financial Inclusion Standing—A new Composite Index”, prepared by Goran Amidžić, Alexander Massara, and André Mialou, authorized for distribution by Luca Errico, February 2014 7. European Commission, “Financial inclusion: ensuring access to a basic bank account. Consultation Document”, Brussels 2009 8. European Foundation for Financial Inclusion”Financial inclusion and new means of payment. Qualitative review in five European countries”, May 2013 9. EMN Magazine, “Migrant’s financial inclusion in the EU:which role for the microfinance sector?”, March 2013 10. “Migrants and financial services”, Luisa Anderloni, Daniela Vandone, University of Milan Revista Română de Statistică - Supliment nr. 1 / 2016 117 11. United nations conference on trade and development(UNCTAD), “Remittances and Financial Inclusion”, February 2015, Mina Mashayekhi Head Trade Negotiations & Commercial Diplomacy Branch DITC/UNCTAD 12. “Migration and Development in the policies of the European Union: trends toward a cosmopolitan approach”, by Flavia Piperno, researcher CeSPI, November 2014 13. National Observatory for financial inclusion of migrants, “Good practices for financial inclusion. A European overview”, June 2013 118 Romanian Statistical Review - Supplement nr. 1 / 2016 Condiţii pentru prezentarea materialelor spre publicare Lucrările ştiinţifice sau tehnice, originale, se pot prezenta redacţiei spre publicare fie sub formă de articole, fie sub formă de scurte comunicări în limba română şi în limba engleză (traducere integrală). Precizările privind condiţiile tehnice pentru predarea materialelor se află pe site-ul www.revistadestatistica.ro, secţiunea „Procesul de recenzare”. 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