Slide 1

INTRODUCTION CLRM, GLRM and SUR models make the following assumption: The error term is uncorrelated with each explanatory variable. Three important sources that produce a correlation between the error term and an explanatory variable 1) Omission of an important explanatory variable 2) Measurement error in an explanatory variable 3 ) Reverse causation A SEM is one which has two or more equations with one variable explained in one equation appearing as an explanatory variable in other equation(s). Slide 2 Purpose Why SES? To investigate the importance of FDI for economic growth in India Time period: 1999-00 to 2011- 12 Bi directional connection between FDI and economic growth Incoming FDI stimulates economic growth and in its turn a higher GDP attracts FDI Slide 3 Model 1. Growth = a1 + a2*(GCFC) + a3*(FDI) + a4*Export + a5*Labor 2. FDI = b1 + b2*Growth + b3*GCFC + b4*(Wage) 3. GCFC = c1 + c2*FDI + c3*Growth + c4*M3 4. Export = d1 + d2*Growth + d3*EXRATE + c4*GCFC Reference: FDI and Economic Growth - Evidence from Simultaneous Equation Models, G Ruxanda, A Muraru - Romanian Journal of Economic Forecasting, 2010. http://www.ipe.ro/rjef/rjef1_10/rjef1_10_3.pdf Slide 4 Classification of Variables Endogenous : Growth rate of GDP, Gross fixed capital formation, Exports, FDI Exogenous : Growth rate of labour, Wage, Exchange rate, M3 money base growth Slide 5 Identification M - No. of excluded exogenous explanatory variables N * - No. of included endogenous explanatory variables 1. First equation : M - Wage, Exchange rate, Deviation of M3 N * - Gross fixed capital formation, FDI, Exports M = N * = 3 => Exactly Identified Slide 6 2. Second Equation : M - Labour growth, Exchange rate, Deviation of M3 N * - GDP growth rate, Gross fixed capital formation M (3) > N * (2) and hence overidentified 3. Third Equation : M - Labour growth, Exchange rate, Wage N * - GDP growth rate, FDI M (3) > N * (2) and hence overidentified 4. Fourth Equation: M - Labour growth, Deviation of M3, Wage N * - GDP growth rate, Gross fixed capital formation M (3) > N * (2) and hence overidentified Slide 7 Estimation of the Model Why not OLS ? Correlation between the random error and endogenous variable OLS estimator biased and inconsistent One situation in which OLS is appropriate is recursive model Slide 8 OLS Estimation GROWTH EQUATION VariableLabelDFParameter Estimate S.Et ValuePr > |t| Intercept 1-44.576213.301-3.350.0016 GCFC 114.289333.74733.810.0004 FDI 1-0.629650.5258-1.200.2372 Export 10.998982.59200.390.7017 Labor 19.315659.00541.030.3062 FDI EQUATION VariableLabelDFParameter Estimate S.Et ValuePr > |t| Intercept 1-8.817742.14 275 -4.120.0002 Growth 1-0.037320.03 527 -1.060.2953 GCFC 12.448400.71 752 3.410.0013 Wage 11.211120.31 025 3.900.0003 proc syslin data = sasuser.Consa 2sls reduced; endogenous Growth GCFC FDI Export; instruments Labor Wage M3 EXRATE; First: model Growth = GCFC FDI Export Labor; Second: model FDI = Growth GCFC Wage; Third: model GCFC = FDI Growth M3; Fourth: model Export = Growth EXRATE GCFC; run; Slide 9 OLS Estimation GFCF EQUATION VariableLabelDFParameter Estimate S.Et ValuePr > |t| Intercept 12.865000.2520 5 11.37 |t| Intercept136.82287.759814.75 2SLS (First Stage) GCFC EQUATION VariableDFParameter Estimate Standard Error t ValuePr > |t| Intercept1-6.104871.83106-3.330.0017 Labor1-1.119091.32569-0.840.4029 Wage11.3550420.295284.59 |t| Intercept 1-193.8392.3074-2.10.0411 GCFC 136.932218.671.980.0538 FDI 1-4.70382.81752-1.670.1017 Export 123.406320.90071.120.2685 Labor 196.301660.77131.580.1198 FDI EQUATION VariableDFParameter Estimate S.Et ValuePr > |t| Intercept 1-11.743.21839-3.650.0007 Growth 1-0.07840.06579-1.190.2391 GCFC 13.458891.109563.120.0031 Wage 11.021430.353252.890.0057 Slide 19 2SLS (Whole Model) GFCF EQUATION VariableDFParameter Estimate S.Et ValuePr > |t| Intercept1 3.181210.337329.43 |t| Intercept 1 -3.60171.00783-3.570.0008 Growth1 0.074450.048161.550.1287 EXRATE1 0.045290.020062.260.0285 GCFC1 1.065740.445722.390.0208 Slide 20 3SLS (Whole Model) proc syslin data = sasuser.Consa 3sls; endogenous Growth GCFC FDI Export; instruments Labor Wage M3 EXRATE; First: model Growth = GCFC FDI Export Labor; Second: model FDI = Growth GCFC Wage; Third: model GCFC = FDI Growth M3; Fourth: model Export = Growth EXRATE GCFC; run; GROWTH EQUATION VariableDFParameter Estimate S.Et ValuePr > |t| Intercept 1-130.5487.7701-1.490.1436 GCFC 129.244518.51521.580.1209 FDI 1-5.29622.68594-1.970.0545 Export 113.395818.63720.720.4758 Labor 131.948353.60440.60.554 FDI EQUATION VariableDFParameter Estimate S.Et ValuePr > |t| Intercept 1-16.7962.23752-7.51 3SLS (Whole Model) GFCF EQUATION VariableDFParameter Estimate S.Et ValuePr > |t| Intercept12.802960.2797310.02 |t| Intercept 1-3.49081.00532-3.470.0011 Growth10.0830.04811.730.0908 EXRATE10.048110.020022.40.0202 GCFC10.975010.445132.190.0334 Slide 22 Comparison - 2SLS and 3SLS GROWTH EQUATION VariableS.E (3SLS) S.E (2SLS) Intercept 87.770192.3074 GCFC 18.515218.67 FDI 2.685942.81752 Export 18.637220.9007 Labor 53.604460.7713 FDI EQUATION VariableS.E (3SLS) S.E (2SLS) Intercept 2.237523.21839 Growth 0.058020.06579 GCFC 0.764581.10956 Wage 0.301450.35325 GFCF EQUATION VariableS.E (3SLS) S.E (2SLS) Intercept0.27973 0.33732 FDI0.03682 0.0374 Growth0.00925 0.00934 M30.2066 0.26195 EXPORT EQUATION VariableS.E (3SLS) S.E (2SLS) Intercept 1.00532 1.00783 Growth0.0481 0.04816 EXRATE0.02002 0.02006 GCFC0.44513 0.44572 Slide 23 Zellner and Theils Equivalence 3 SLS on whole model= 3 SLS on OID equations (Zellner and Theils, 1962) 3SLS on EID= 2SLS+ linear equation of the OID equations (Zellner and Theils, 1962) Slide 24 3SLS (OID Equations) proc syslin data = sasuser.Consa 3sls; endogenous Growth GCFC FDI Export; instruments Labor Wage M3 EXRATE; Second: model FDI = Growth GCFC Wage; Third: model GCFC = FDI Growth M3; Fourth: model Export = Growth EXRATE GCFC; run; FDI EQUATION VariableDFParameter Estimate S.Et ValuePr > |t| Intercept 1-16.7962.23752-7.51 3SLS(EID) vs 2SLS(EID) GROWTH EQUATION (3SLS) VariableDFParameter Estimate S.Et ValuePr > |t| Intercept 1-130.5487.7701-1.490.1436 GCFC 129.244518.51521.580.1209 FDI 1-5.29622.68594-1.970.0545 Export 113.395818.63720.720.4758 Labor 131.948353.60440.60.554 GROWTH EQUATION(2SLS) VariableDFParameter Estimate S.Et ValuePr > |t| Intercept 1-193.8392.3074-2.10.0411 GCFC 136.932218.671.980.0538 FDI 1-4.70382.81752-1.670.1017 Export 123.406320.90071.120.2685 Labor 196.301660.77131.580.1198 Slide 27 Data Variable in ModelActual Variable RequiredDenominationFrequency Growth Rate GDP figures at Factor Cost and Constant Prices RupeesQuarterly Gross Fixed Capital Formation as proportion to GDP Gross Fixed Capital Formation%ageAnnual Export as proportion to GDPExportRupeesMonthly GDP figures at Factor Cost and Current Prices RupeesQuarterly Labor Force GrowthPopulation(millions)Annually Wage Growth Inflation based on Consumer Price Index %ageMonthly M3 GrowthM3 Money stockRupeesMonthly Exchange Rate Rupees vs DollarMonthly Slide 28 Data Actual VariableSite GDP figures at Factor Cost and Constant Prices http://dbie.rbi.org.in/DBIE/dbie.rbi?site=home Reserve Bank of India GDP figures at Factor Cost and Current Prices Export Population M3 Money stock Exchange Rate Inflation based on Consumer Price Indexhttp://labourbureau.nic.in/indexes.htmhttp://labourbureau.nic.in/indexes.htm (Ministry of Labor) Gross Fixed Capital Formationhttp://www.indexmundi.com/facts/india/gross-fixed-capital-formation Slide 29 Limitations Number of data points are small. (only 13 years) Lag Values ignored in each of the equation Proxy for labor(population), wage growth(CPI inflation) were used which might not reflect the true relation between the variables There are other factors which affect inflow of FDI but are hard to quantify such as govt policies, economic and political stabilities etc and hence are ignored in current work. Slide 30 Thank You!