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Academy of Economic Studies Doctoral School of Finance and Banking Romania’s potential growth rates and output gap MSc.: Catalin Condrache Supervisor: Prof. Moisa Altar Bucharest, July 2008 Contents Preliminary aspects Methods for estimating potential GDP Model Definition Modeling Romania’s situation Estimating and testing methodology Conclusions and future research Abstract The present working paper sets a goal to assess the impact of production progress, stock of capital, employment in the economy and human capital, within GDP formation. The approach is slightly different from that used so far in estimating potential GDP in the models for Romania’s production function, because in my opinion the current approach reveals the evolution of the potential GDP more realistically. In order to improve the production function model for Romania, I augmented the model by human capital approximation. Preliminary aspects Definitions Potential GDP – represents the level of real GDP which the economy can produce without generating inflationary pressures. Output Gap – represents the difference, expressed in percentage points, between actual real GDP and potential GDP. Importance The concept of potential GDP plays a key role in understanding the economic long term growth theory. According to this theory the long term growth rate in GDP is explained by fundamentals factors, such as: the structure of the economy, demographic and educational factors, technology, etc. Methods for estimating potential GDP Univariate Methods Hodrick – Prescott Filter Band Pass Filter Models with unobserved components - Kalman Filter Multivariate Methods Production Function, Cobb – Douglas Multivariate unobserved components models Structural Vector Autoregression model Difficulties in estimating potential GDP Short sample of usable data for Romania Structural changes happened during the analyzed period Official GDP data is published with a lag, being subsequently subject to revision Unreliable statistical data for capital stock Model Definition The types of production function used in the literature are particular forms of the constant elasticity of substitution –CES In all models, the Cobb – Douglas production function is used (1 ) The following equation seems to be a better approximation: Yt At K t Lt Yt At K t H t L1t In order to surprise the dynamic of GDP we take the log log Yt log At log K t log H t (1 ) log Lt Modeling Romania’s situation Data series: quarterly 1998 Q1 -2007 Q4 Real GDP (expressed in 2000 ct price) Gross Formation of Fixed Capital (2000 ct price) - GFFC Real accumulated capital Employment in the economy Human Capital Modeling Romania’s situation Challenge –estimation of capital stock Harberger (1978)- assumes a capital growth rate equal to the average growth rate of real GDP. Kt It ; (g ) K1 = K0 x (1 – φ) + I1 g = 4.70% (average growth rate of real GDP – for the period considered) Φ = 5.0% (depreciation of fixed capital) Modeling Romania’s situation Evolution of labor force – structural brake Adj Labor Force Unadj Labor Force 12,000,000 9,800,000 9,600,000 10,000,000 8,000,000 9,400,000 9,200,000 9,000,000 6,000,000 4,000,000 8,800,000 8,600,000 8,400,000 2,000,000 8,200,000 8,000,000 7,800,000 19 98 19 Q1 98 19 Q3 99 19 Q1 99 20 Q3 00 20 Q1 00 20 Q3 01 20 Q1 01 20 Q3 02 20 Q1 02 20 Q3 03 20 Q1 03 20 Q3 04 20 Q1 04 20 Q3 05 20 Q1 05 20 Q3 06 20 Q1 06 20 Q3 07 20 Q1 07 Q 3 19 98 Q 19 1 98 Q 19 4 99 Q 20 3 00 Q 20 2 01 Q 20 1 01 Q 20 4 02 Q 20 3 03 Q 20 2 04 Q 20 1 04 Q 20 4 05 Q 20 3 06 Q 20 2 07 Q 20 1 07 Q 4 - The series was adjusted by assuming zero growth between 2001 Q4 – 2002 Q1, and the data prior to 2001 Q4 was recursively corrected using the quarterly difference taken from the data based on the previous methodology Modeling Romania’s situation The following data sets likely to figure as human capital: Share of capital education expenditure in GDP Share of employed with secondary and university education in the employment group over 15 years of age (Eurostat data; in line with levels 3 – 6 of ISCDE 1997) Share of employed with university education in the employment group over 15 years of age Share of employed with secondary and university education in employment group over 25 years of age Share of employed with university education in the employment group over 25 years of age Share of male with secondary and university education in the employment group over 15 years of age Estimating and testing methodology Census – X12 algorithm has been used to seasonally adjust all time series The employment in the economy data series was adjusted for structural break In order to surprise the dynamic of GDP, I take a log of the Cobb – Douglas function Modeling and testing methodology The firs estimation showed a Durbin–Watson =0.536 (autocorrelation) Remedy for serial correlation: Cochrane-Orcutt Yt 1 2 K t 3 Lt 4 H t t t t 1 t ρ = 0.723194. Variable Coefficient RHO(-1) 0.732194 Std. Error t-Statistic 0.121095 6.04643 Prob. 0 Modeling and testing methodology New error estimate t t t 1 New equation Yt Yt 1 1 (1 ) 2 ( K t K t 1 ) 3 ( Lt Lt 1 ) 4 ( H t H t 1 ) t Yt* 1* 2 K t* 3 L*t 4 H t* t After all the adjustments were implemented, I obtained LogGDP = 1.673 + 0.29*LogK + 0.11*LogL + 0.62*LogH New DW = 1.975663 Modeling and testing methodology The coefficients are statistically different from zero at a 5% significance level The percentage of the total variation in the dependent variable explained by the independent variables, R2, is at a good level of 84% By adding the human capital to the C–D production function the R-squared has improved, increasing the accuracy of the forecasting Constant returns to scale assumption, was tested using Wald test Wald Test: Equation: COBB_DOUGLAS T-Statistic Value F-statistic 0.007611 Chi-square 0.007611 Null Hypothesis Summary: Normalized Restriction (= 0) -1 + C(2) + C(3) + C(4) df (1, 36) 1 Probability 0.931 0.9305 Value 0.016221 Std. Err. 0.185927 Modeling and testing methodology Normality test 7 Series: Residuals Sample 1998Q2 2007Q4 Observations 39 6 5 4 3 2 1 0 -0.04 -0.02 0.00 Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis -3.68e-16 0.000182 0.021764 -0.037896 0.014463 -0.448283 2.620932 Jarque-Bera Probability 1.539725 0.463077 0.02 The residuals seems to be almost normally distributed Kurtosis is almost 3 Skewness is very close to zero Jarque-Berra – is at a small value Calculating potential growth rates and output gap Resulted production function LogGDP = 1.673 + 0.29*LogK + 0.11*LogL + 0.62*LogH The Total Factor Productivity (TFP) has the major impact According to the GDP regression function, in order to create sustainable economic growth for the medium term, the solution is to rise human capital and stock of capital mainly Country like Romania would attract capital and loose qualified labor force Calculating potential growth rates and output gap Growth rates of real GDP and potential GDP 10.00% 10.00% 8.00% 8.00% 6.00% 6.00% 4.00% 4.00% 2.00% 2.00% 0.00% 0.00% -2.00% 1999 2000 2001 2002 2003 2004 2005 2006 2007 -1.16% 0.88% 5.72% 5.10% 5.15% 8.45% 4.22% 7.86% 6.05% Potential GDP 2.00% 3.20% 4.50% 5.10% 5.30% 5.70% 5.90% 6.00% 6.20% Real GDP -2.00% Calculating potential growth rates and output gap Output –gap 0.00% -0.50% -1.00% -1.50% -2.00% -2.50% -3.00% -3.50% -4.00% -4.50% -5.00% 1998 Output-gap -3.45% 1999 2000 2001 2002 2003 2004 2005 2006 2007 -3.62% -4.45% -3.34% -3.34% -3.48% -0.97% -2.54% -0.83% -0.97% Conclusions and future research The results show an increasing annual potential GDP growth rate, from an average of 3.70% in the period 1998 – 2002, to values of around 6% in recent period Romania experienced in the past 10 years, a potential GDP growth rates above the those registered by new EU Central and Eastern European member states in their periods of high growth The factors with the biggest impact in growth rates of potential GDP are total factor productivity, human capital and stock of capital Estimating the production function, was the first step in understanding and analyzing real convergence The convergence process concept has its origin in the exogenous model of growth of Robert Solow. According to it, the existence of some economies that have similar characteristics in terms of preferences and technologies, of some declining marginal efficiency as well as of a perfect flexibility from the production factors generates a reduction of the incomes differences between the countries (regions) Thank you for your consideration! 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