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!