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PMA WORKING PAPER WP/12/05 A SHORT RUN REDUCED FORM EQUATION FOR REAL GDP GROWTH IN OPT Mohammed Aref and Michel Dombrecht PMA Working Paper Research and Monetary Policy Department A SHORT RUN REDUCED FORM EQUATION FOR REAL GDP GROWTH IN OPT Prepared by Mohammed Aref1 and Michel Dombrecht2 April 2012 Abstract The paper aims at designing (semi) reduced form equations for quarterly Real Gross Domestic product (RGDP) growth in the Occupied Palestinian Territory (OPT). This need was based on the observation that not sufficiently long time series exist to build a structural quarterly model. Main result of this paper show that the quarterly RGDP is very much driven by some of the exogenous variables identified in the PMA’s annual structural models. Among these, the external environment was found to play a dominating role. Besides external influences, quarterly RGDP growth is also driven by construction of new buildings activity for which the issued new building licenses are a reliable quarterly indicator. These models have been used to produce out of sample quarterly RGDP forecasts over the period 2012 – 2013. These point forecasts were accompanied by alternative optimistic and pessimistic scenario’s. © April, 2012 All Rights Reserved. Suggested Citation: Palestine Monetary Authority (PMA), 2012. A SHORT RUN REDUCED FORM EQUATION FOR REAL GDP GROWTH IN OPT Ramallah – Palestine All Correspondence should be directed to: Palestine Monetary Authority (PMA) P. O. Box 452, Ramallah, Palestine. Tel.: 02-2409920 Fax: 02-2409922 E-mail: [email protected] www.pma.ps JEL Classification Numbers: C22; E20; E27 Keywords: Reduced form equation; ARDL; Economic growth Authors’ E-mail addresses: [email protected]; [email protected] 1 2 Palestine Monetary Authority. University of Antwerp, Belgium and Hogeschool Universiteit Brussels, Belgium. Thanks to our colleague Mahmoud Bsharat for his assistance 1 CONTENTS 1. Introduction 5 2. The Model 5 2.1 A truly exogenous mode 9 2.2 A semi-reduced form equation 14 3. GDP Forecast and Scenario Analysis 15 4. Conclusion 17 2 List Of Tables: Tables 1: Frequency and Start Data of Main Exogenous Variables for Real OPT VARIABLES FOR REAL OPT GDP 7 Tables 2: Unit Root Test of Variables in Levels and First Difference 8 Tables3: OLS Simple Regression for Real GDP in OPT for the Period 2001Q1 2011Q4 12 Tables 4: CHOW’ Frequency Breakpoint Test 12 Tables 5: The Equilibrium Correction of ARDL (1,1,1) RGDP Equation 13 List Of Figures: Figure 1: Index of Real GDP in OPT and The Exogenous Variable 9 Figure 2: The Estimated Trend of The GDP Ratio 11 Figure 3: Actual and Fitted Real GDP in OPT 11 Figure 4: The Baseline Forecast of Quarterly Real GDP in OPT 14 Figure 5: Baseline Forecast For Building Licenses 15 Figure 6: Forecast in Different Scenarios for Quarterly Real GDP in OPT 16 3 Abbreviations: CIM$ The weighted average prices set by foreign suppliers on the domestic market (cost of imports) PWFPI World food price index FTAID Grants and donations RGI Real Government Investment NCDT Number of clousre days for trade BL Building licenses (new builiding) EIS Employment in Israel and settlement DDAWIS Daily average wage in Israel and Settlements MFFRUS US Federal Funds rate NGS Nominal Government Spending MEXGRISINDEX Exchange rate index (USD/NIS) CPI Consumer price idex in Palestine PCPIP$ Consumer price index adjusted to USD REER Real effective exchange rate OPT Occupied Palestinian Territory PMA Palestine Monetary Authority RGDP Real Gross Domestic Product in Palestine RGDPI Real Gross Domestic Product in Israel LRWGDPAIMEX The weighted average real GDP in Palestinian Territory main export and import markets 4 1. Introduction The PMA’s structural and financial programming models are on an annual frequency. The main reason for this is that most of the data used in those models are not available on quarterly frequency. Such annual models are extremely useful for the analysis of the effects of all kind of shocks on the economy and for policy analysis. They also provide forecasts for many other macroeconomic variables in a way that is coherent and consistent with the real GDP forecast. Such models also allow to build up the narrative of the forecast and provide for a coherent framework to explain recent developments, the medium term economic outlook and the main transmission mechanisms of external and internal shocks. On the other hand, for the purpose of short term real GDP growth forecasting the annual models may miss at least part of the influence of the short term economic dynamics and their short term forecasts may therefore be sub-optimal. In most central banks (as well as outside central banks) forecasts are the result of a mixture of information, among which the above mentioned models. Apart from the structural models, forecasts also tend to rely on other available information such as those present in single and composite business cycle indicators, either of quantitative or qualitative nature. An alternative is to use the information present in the annual structural model and to explore the main driving forces in that model and to verify which of the data among them are available on a higher frequency. This is the approach we follow in this paper. Yet another alternative would be to estimate real GDP equations using available high frequency indicators which are not necessarily present in the structural models. Such estimations may then be used to construct a business cycle indicator, but this a-theoretical approach will be explored in a separate paper. 2. The Model The supply side of an economy basically reflects the forces behind firms' production of goods and services, their demand for factors of production, their price setting behaviour and the structural characteristics of wage negotiations between employers and employees. Firms produce goods and services on the basis of a well defined production technology, Michel, Aref 5 and Khalil (2011). This technology is assumed to be Cobb-Douglas. This implies that in the long run the shares of both factors of production, capital and labour, in total income (value added at factor cost) remain constant. This is a fairly reasonable assumption since it is not realistic to assume that one of those shares would continuously increase or decline. Forces would come into play to restore the normal shares of labour and capital incomes in the economy's total value added. On other hand, demand is driven by real income growth, which is itself sourced in the labour productivity growth which originates in the supply environment of the economy. In a world of perfect competition, prices would be solely determined by intersections of demand and supply and the mark up would be zero. In most countries markets are basically characterised by imperfect competition, more in particular by so called 'monopolistic competition'. Therefore the demand for goods and services is mainly driven by the real income growth of all sectors in the economy and by the price setting behaviour of profit maximizing firms. The main demand components are consumption by households, private investment expenditures, government expenditure, exports and imports. These elements of the structural model can be integrated so as to obtain a reduced form model in which real GDP is only a function of the main exogenous variables. A similar approach has been applied by, among others, Kandil, Mirzaie (2003) and Bahmani-Oskooee, Kandil (2007). Both papers used this kind of approach to analyze the effects of exchange rate fluctuation on output and prices in developing countries. Zeng (2011) evaluates different approaches to growth forecasts for Korea where the initial selection of the indicators is driven by economic theory. In the structural models developed for OPT the following main exogenous variables can be found, which together with their available highest frequency and earliest start dates are presented in table 1. As to the external demand and supply shocks, special attention can be drawn to the links between the economies of OPT and Israel: Israel’s weight in OPT’s foreign demand variable (real GDP in OPT’s main export markets, weighted by each country’s weight in OPT’s international trade) is very high; A substantial proportion of the OPT labour force is employed in Israel, 6 The daily average wage rate of employees employed in Israel is larger than wages in local employment, Israel has a very high weight in the calculation of OPT’s NEER, REER and CIM; Table 1: Frequency and start data of main exogenous variables for real GDP in the OPT Item Model variable name Highest frequency Start date Source Monthly Monthly 1997M1 PMA IMF Annual Annual 1996 1994 External price Cost of imports World food price index CIM$ PWFPI Fiscal Grants and donations Real Government investment FTAID RGI External demand and supply Number of closure days for trade Real world GDP Received private sector transfers from abroad NCDT RWGDPAIMEX GREMIT Quarterly Quarterly Annual 2000Q4 2000Q1 1998 UNSCO PMA BOP Employment in Israel and settlements EIS Quarterly 1996 PCBS DDAWIS$ Quarterly Daily average wage of Palestinian workers in Israel and Settlements PCBS Financial market Exchange rate index (USD/NIS) US Federal Funds rate MEXGRISINDEX MFFRUS Monthly Monthly 1997M1 PMA PMA Quarterly Monthly Monthly - 2000 Q1 PCBS PCBS PMA PMA Other needed Real GDP Consumer Price Index Real Effective Exchange Rate Deterministic trend and seasonal dummy’s RGDP PCPIP$ REER T, SD1, SD2, SD3 1997M1 - Other potentially interesting indicators relating to domestic demand and supply shocks but which are not included among the exogenous variables mentioned in table 1 are: For fiscal shocks: Quarterly public employment, quarterly daily average wage in the public sector; Other: building licenses, especially the number of licenses of new buildings. In what follows we will build a quarterly real GDP models for OPT based on the above mentioned indicators that are found to have a significant impact on economic growth in OPT. These models will then be used for generating a baseline forecast as well as a pessimistic and 7 optimistic scenarios. All candidate Relevant variables to be used in the analysis were found to be integrated of order 1 (see table 2). Hence Augmented Dickey Fuller (ADF), and Phillips Perron (PP) were used to test for the stationary of those variables. Table 2: Unit root test * Variables Level LCIM 1st difference Level LCIM$ 1st difference Level LCPI 1st difference Level LDAWIS 1st difference Level LEIS 1st difference Level LNCDT 1st difference Level LNGS 1st difference Level LRDAWIS 1st difference Level LREER 1st difference Level LRGDP 1st difference Level LRGDPI 1st difference Level LWFP 1st difference Level LBL 1st difference * Presented values represent the P-value. ADF 0.1385 0.0010 0.0044 0.0008 0.8711 0.0002 0.7955 0.0000 0.7723 0.0000 0.1501 0.0000 0.7142 0.0000 0.6426 0.0001 0.9436 0.0002 0.9289 0.0000 0.9703 0.0000 0.8603 0.0000 0.0560 0.0000 PP 0.1929 0.0007 0.0245 0.0013 0.8591 0.0037 0.8190 0.0000 0.7059 0.0000 0.1670 0.0000 0.6397 0.0000 0.6522 0.0002 0.9436 0.0002 0.9724 0.0000 0.9980 0.0000 0.9528 0.0000 0.0500 0.0000 2.1 A truly exogenous model In this kind of approaches, real quarterly GDP is modeled by using exogenous variables only. As for quarterly GDP in the OPT, being a small open economy, the external environment is the natural supplier of such exogenous variables on which the local policy makers or economic agents have no control. As mentioned above, the external environment in OPT is very much conditioned by political, as well as, economic developments and conditions in Israel. Our basic reduced form model is therefore based on the variables shown in Figure 1 which shows a likelihood of finding a co-integrating vector between these variables. 8 Figure 1: Index of Real GDP in OPT and the exogenous variable 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 RGDP Israel 2005=1 RGDP PT 2005=1 EYMPLOYMENT IN ISRAEL 2005=1 As can be seen from the above Figure, real GDP in OPT is much more volatile as compared to Israel. This illustrates the numerous and severe shocks to which the OPT economy has been exposed in the past. It is also clear that the period 2004 – 2005 was relatively prosperous period in OPT, whereas part of 2006 up till early 2009 the ratio3 was under the period average. Since then the growth rate of real GDP in OPT has outpaced that of Israel. The annual real GDP growth rate showed obvious fluctuations during the period (2001 – 2011), the study period due to the political conditions that OPT lived under . This period, has witnessed deep political, economic, social, and security developments, which still have direct implications on the economic performance in general, and on living standards and welfare of the Palestinian people. After the relative stability and the start of recovery of the Palestinian economy in 2004, following the negative economic implications in the previous period of re-occupation of West Bank (WB) in 2002, the Palestinian economy deteriorated further in the wake of the legislative and presidential elections in 2006. As a result of the elections outcome in favor of Hamas, international aid stopped and Israel refused to transfer tax revenues to the Palestinian Authority (PA), which led to a financial distress and the inability of the Palestinian 3 the ratio = RGDP index in OPT divided by RGDP index in Israel 9 government to pay salaries for more than 15 months. This political and financial siege continued until the mid of 2007. Then came the Palestinian division after Hamas takeover of Gaza in June 2007. An emergency government was formed in Gaza Strip (GS), while a caretaker government was instituted in the WB. This led to the lifting of the political and financial siege from WB and a tightening of the siege in GS. Consequently the economic and social conditions deteriorated in GS but improved in WB. In 2008, Israel waged a war on GS and inflicted substantial casualties and material damage, thus worsening the economic and social conditions. With the comprehensive and tightened siege on GS for years, the financial requirements on the Palestinian economy to rebuild what was destroyed in the war and to address the serious structural effects in the Gaza economy soared. Then a period of relative stability in all political, economic, and security aspects, especially in the WB, followed and a reform plan for the period (2008 – 2010) was developed. The plan led to an improvement in the economic performance, particularly in the public finance and spending rationalization, and the reduction of the budget deficit. In the WB, this was accompanied by development of the infrastructure and an improved investment environment. However, the plan did not achieve the desired objectives in GS because of the division and the tightened Israeli siege and the almost complete prevention of the entrance of construction material for reconstruction. As result of all above circumstances the annual real GDP growth rate showed obvious fluctuations during 2001 – 2011, but In general, the average annual real GDP growth rate reached 4.3 percent during this period. Following the theoretical ‘convergence hypothesis’, one would expect growth per capita to be higher in OPT as compared to Israel which is already in a more advanced stage of economic 10 welfare as measured by the level of real income per capita. In other words, over the long run we would expect a positive trend in the RGDP-ratio. Figure 2: The estimated trend of the GDP ratio 1.15 1.1 1.05 1 0.95 0.9 0.85 0.8 0.75 RGDP ratio Average There is indeed a positive trend, which is not necessarily the result of a higher growth rate of GDP per capita, but could also be the result of higher employment (population) growth. But again, during end-2003 up till end of 2005, the ratio was significantly above trend and conversely from mid-2006 till start of 2009. The estimation of the quarterly real GDP model for OPT will have to take these structural breaks into account. Figure 3: Actual and Fitted Real GDP index in OPT 1.2 1.1 1 0.9 0.8 0.7 0.6 Actual Fitted As a starting point, we estimated a long run equation for real GDP in OPT as a function of real GDP in Israel and the number of OPT residents working in Israel (and settlements). The loglinear equation was estimated as: 11 Table 3 : OLS simple regression for real GDP in OPT (RGDP) For the period 2001Q3 2011Q4 Variable C LRGDPI LEIS F-statistic Durbin-Watson Coefficent -7.6753 1.0186 0.2287 0.867 135.6 0.634 Standard Error 1.4166 0.1921 0.0972 Prob. 0.0000 0.0000 0.0238 We tested this equation for the presence of structural breaks using Chow’s Breakpoint test. As can be seen from table 4 the null hypothesis of no structural breaks during the periods mentioned is rejected by all test statistics mentioned in the table. Table 4: Chow’ Breakpoint test Chow Breakpoint Test 2003Q4 2005Q4 2006Q3 2009Q1 Null Hypothesis No breaks at specified breakpoints Varying regressors All equation variables Equation Sample F-statistic Log likelihood ratio Wald Statistic 2001Q3 2011Q4 10.804 73.843 129.64 Prob. F(12,27) Prob. Chi-Square (12) Prob. Chi-Square (12) 0.0000 0.0000 0.0000 Given these findings we estimated a dynamic equation which was derived from the ARDL technique (see Khalil and Michel, 2011). To determine the appropriate lag length (P), we estimated this conditional equilibrium correction model (ECM ) by OLS for P = 1, 2, 3, 4, 5,6 over the period 2001Q3 to 2011Q4. Then we tested the Akaike (AIC) and Schwarz Bayesian Information Criteria (SBC) for all lags. These are statistics calculated in E-views which deviate from the formulas in PSS, although both are essentially based on the log likelihood parameter. 12 Results show a small differences between these criteria underlying the choice of the lag order of the dynamic equation, we have adopted a parsimonious approach and we will therefore concentrate on the most simple (P = 1) model. ∆ 7.749 ∆ 0.741 0.189 ∆ 2006 3 2009 1 The error correction term 0.916 0.181 0.316 ∆ 0.056 ∆ 0.076 2003 4 2005 4 0.873 0.067 1 can be written as follows: 2 Where, 0.741, 0.916, 0.181 The long-run relationship between LRGDP and LRGDPI and LEIS can be written as follows: 1.29 0.244 3 Table 5: The equilibrium correction of ARDL (1,1,1) RGDP equation Regressor Coefficient Standard Error ‐0.7408 0.1208 0.9164 0.1829 0.1813 0.0696 ∆ 0.8727 0.3182 ∆ 0.1894 0.0611 Intercept ‐7.7493 1.4174 Dum2006q32009q1 ‐0.0674 0.0171 Dum2003q42005q4 0.0762 0.0182 0.5510 3.5214 3.1901 P-Value 0.0000 0.0000 0.0136 0.0097 0.0039 0.0000 0.0004 0.0002 2.2 A semi - reduced form equation As an alternative to the approach discussed above, we will extend the analysis by including a domestic indicator of economic activity. The variable that was found to be of particular significance in explaining economic activity in OPT was the number of issued licenses for new buildings. In many countries this is, found to be a reliable indicator to measure recent 13 developments in the building sector, which is known to generate multiplier effects on the rest of the economy. Again we used the ARDL technique to test for a significant levels relationship and to estimate the dynamics of the relationships between quarterly RGDP in OPT, RGDP in Israel, number of Palestinian employees in IS and the number of licenses for new buildings in OPT. ∆ 7.958 0.826 0.962 0.639 ∆ 0.127 ∆ 2006 3 2009 1 0.0538 0.143 0.103 0.111 ∆ 0.0406 2003 4 2005 4 4 3. GDP Forecasts and Scenario Analysis We used the models explored above to generate a real GDP forecast for OPT for the period 2012 – 2013. The baseline forecast is based on the simple reduced form equation, taking into account the most recent real GDP forecast for Israel produced by the BOI. BOI forecasts RGDP in Israel to grow by 3.1% in 2012 and 3.5% in 2013. The result of the baseline forecast for real quarterly GDP in OPT is shown in Figure 4. This forecasts implies an annual RGDP grow by 6.1% in 2012 and 6.2% in 2013. Figure 4: the Baseline forecast of quarterly real GDP in OPT 2000 Million US$ 1800 1600 1400 1200 1000 800 600 RGDP RGDPF 14 This baseline forecast does not include an explicit scenario for the activity in the building sector (number of licenses for new buildings). We inverted the second equation reported above to calculate the implied forecast for building licenses implied by the baseline forecast. 7.958 0.8258 0.0075 0.639 ∆ 2006 3 2009 1 0.0538 01 / 0.1105 0.9617 0.1432 0.1275 ∆ 0.111 ∆ 2003 4 2005 4 5 0.0406 Figure 5 Shows that the baseline RGDP forecast implies slowing growth of construction activity (BL_0). On the other hand building activity shows a strong upward trend since 2009. Figure 5: Baseline forecast for building licenses 2000 1600 1200 800 400 0 Building licenses new builidings (estimate for 2008Q1) BL_0 As an alternative (optimistic) scenario we have extrapolated this recent trend in building activity into the future as shown in Figure 6. In this optimistic scenario real GDP in OPT would grow by 8.8% in 2012 and by 8.3% in 2013. 15 Figure 6: Forecast in different scenarios for quarterly real GDP in OPT 2500 2000 1500 1000 500 0 RGDP Baseline Optimistics Pessimistics As the OPT economy has been subjected to severe shocks due to the particular political environment, a pessimistic scenario was designed that takes into account the kind of situation prevailing in part of 2006 up till early 2009. We reactivated that dummy variable from the second half of 2012 onwards. Under this pessimistic scenario, the OPT economy would grow by 2.2% in 2012 and by 0.8% only in 2013. The latter figures indicate drastic drop in the growth rate under the pessimist scenario versus the baseline scenario. 16 4. Conclusion The objective of this paper was to design (semi) reduced form equations for quarterly RGDP growth In OPT. This need was based on the observation that not sufficiently long time series exist to build a structural quarterly model. It was found that quarterly RGDP is very much driven by some of the exogenous variables identified in the PMA’s annual structural models. Among these, the external environment was found to play a dominating role. Besides external influences, quarterly RGDP growth is also driven by construction of new buildings activity for which the issued new building licenses are a reliable quarterly indicator. These models have been used to produce out of sample quarterly RGDP forecasts over the period 2012 – 2013. These point forecasts were accompanied by alternative optimistic and pessimistic scenario’s. 17 References Bahmani-Oskooee, M and Kandil, M, “Exchange Rate Fluctuations and Output in OilProducing Countries: The Case of Iran”, IMF, WP/07/113. Kandil, M. and Mirzaie, I., “The Effects of Exchange Rate Fluctuations on Output and Prices: Evidence from Developing Countries”, IMF, WP/03/2000. Michel, D. Aref, M., and Khali, S., 2011, “Analysis of the Supply Side of the Palestinian Territory Economy”, PMA. Pesaran, H., Y. Shin, and R. Smith, 2001, “Bounds Testing Approaches to the Analysis of Level Relationships”, Journal of Applied Econometrics, special issue in honour of J.Sargan on the theme “studies in Empirical Macroeconometrics”, (eds.) D. Hendry and M. Pesaran, Vol.16. Khalil, S., and Michel, D., 2011, “The Autoregressive Distributed lag Approach to Cointegration Testing: Application to OPT Inflation”, PMA. Zeng, L., “Evaluating GDP Forecasting Models for Korea”, IMF, WP/11/53. 18