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INTRODUCTION CLRM, GLRM and SUR models make the following assumption: The error term is uncorrelated with each explanatory variable. . Purpose. Why SES?. To investigate the importance of FDI for economic growth in India Time period: 1999-00 to 2011-12.
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INTRODUCTIONCLRM, GLRM and SUR models make the following assumption: The error term is uncorrelated with each explanatory variable.
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
Model • Growth = a1 + a2*(GCFC) + a3*(FDI) + a4*Export + a5*Labor • FDI = b1 + b2*Growth + b3*GCFC + b4*(Wage) • GCFC = c1 + c2*FDI + c3*Growth + c4*M3 • 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
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
Identification • M∆ ∆ - No. of excluded exogenous explanatory variables • N * - No. of included endogenous explanatory variables • First equation : M∆ ∆ - Wage, Exchange rate, Deviation of M3 N * - Gross fixed capital formation, FDI, Exports M∆ ∆ =N * = 3 => Exactly Identified
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 • Third Equation : M∆ ∆ - Labour growth, Exchange rate, Wage N * - GDP growth rate, FDI M∆ ∆ (3) >N * (2) and hence overidentified • Fourth Equation: M∆ ∆ - Labour growth, Deviation of M3, Wage N * - GDP growth rate, Gross fixed capital formation M∆ ∆ (3) >N * (2) and hence overidentified
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
OLS Estimation proc syslin data = sasuser.Consa2sls 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;
OLS Estimation Growth = -44.5762 + 14.28933*GCFC -0.62965*FDI + 0.99898* Export + 9.31565 * Labor
Methods of estimation • Indirect Least Squares Estimation Method • Two-stage least squares (2SLS) estimation Method • Three-stage least squares (3SLS) estimation Method • Instrumental Variable Method • Limited Information Maximum Likelihood Method(LIML) • Full Information Maximum Likelihood Method(FIML)
2SLS SAS command:procsyslin data = sasuser.Consa 2sls; 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;
3SLS SAS command: 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;
Reduced Form proc syslin data = sasuser.Consa 3sls 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; proc syslin data = sasuser.Consa2sls 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;
2SLS (First Stage) proc syslin data = sasuser.Consa2sls First; 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;
2SLS (Whole Model) proc syslin data = sasuser.Consa2sls; 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;
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;
Zellner and Theil’s Equivalence • 3 SLS on whole model= 3 SLS on OID equations (Zellner and Theil’s, 1962) • 3SLS on EID= 2SLS+ linear equation of the OID equations (Zellner and Theil’s, 1962)
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;
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.