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ijcrb.webs.com INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS JANURAY 2011 VOL 2, NO 9 economic growth and on average, a 1 percent rise in manufacturing exports leads to a 0.165 percent increase in economic growth within a year while one year lagged manufacturing exports are not significant. The negative significant coefficient of semi-manufacturing exports in short run explores very interesting results in explaining economic growth, on average, a 1 percent rise in semi-manufacturing exports leads to a 0.066 percent decline in economic growth within a year. However primary exports are not significant for economic growth. The results of short run dynamic ECM specification suggest that human capital is significant in enhancing economic growth while all components of total exports are not led to increase economic growth except manufacturing exports. Overall, the results of short run dynamic ECM model suggest that both human capital and manufacturing exports are the engine of economic growth for Pakistan. 5.3 Diagnostic Tests To ensure that the estimated parsimonious short run model is consistent with the standard assumptions of OLS, the results are reported in Table 4. a) Normality Test (Jarque-Bera) The results of JB test statistics for normality is not significant, so JB test does not reject the normality hypothesis of errors, indicating that the residuals are normally distributed. b) Serial Correlation Breusch–Godfrey test for serial correlation in residuals was performed up to fourth lag order. There is no significant evidence of the serial correlation in the residuals, so the null hypothesis of no serial correlation in the residuals cannot be rejected up to fourth order, suggesting that lag structure used in this parsimonious model is appropriate. c) ARCH Test F and χ 2 -statistics are used to test for null hypothesis of no Heteroskedasticity in the residuals, the ARCH test is carried out up to fourth lag order, first, third and fourth lag order ARCH test results suggest that the residuals are not Heteroskedastic. d) White Heteroskedasticity Test White‘s Heteroskedasticity test is carried out for null hypothesis of no Heteroskedasticity in residuals, and χ 2 and F-statistics showing that there is no evidence of Heteroskedasticity in residuals. e) Model Specification Test Ramsey‘s RESET test was applied for the model specification. The test fails to reject the null hypothesis of linearity in the parameters, indicating evidence of linearity in the model specification. The RESET test is also a general test for omitted variables and incorrect functional form. The results of Ramsey‘s RESET test suggest that the model specification is appropriate and the parameters of the model are stable. COPY RIGHT © 2011 Institute of Interdisciplinary Business Research 456
ijcrb.webs.com INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS Table 4: Diagnostic Tests Probability Normality Test (Jarque-Bera Statistic) Jarque-Bera Statistic 4.5083 0.1050 Serial Correlation First-Order F-Statistic 0.0544 0.8180 � (Breusch-Godfrey Serial Correlation LM Test) 2 -Statistic 0.0868 0.7683 Second-Order F-Statistic 0.0259 0.9744 � 2 -Statistic 0.0873 0.9573 Third-Order F-Statistic 0.0777 0.9712 � 2 -Statistic 0.4094 0.9383 ARCH Test (Autoregressive Conditional Heteroskedasticity Test) White Heteroskedasticity Test Model Specification Test (Ramsey RESET Test) Fourth-Order F-Statistic 0.2837 0.8844 � 2 -Statistic 2.0025 0.7353 First-Order F-Statistic 0.8832 0.3551 � 2 -Statistic 0.9162 0.3385 Second-Order F-Statistic 3.7147 0.0376 � 2 -Statistic 6.4736 0.0393 Third-Order F-Statistic 0.0777 0.9712 � 2 -Statistic 0.4094 0.9383 Fourth-Order F-Statistic 1.8218 0.1590 � 2 -Statistic 6.7369 0.1505 F-Statistic 0.6186 0.8311 � 2 -Statistic 16.939 0.6569 F-Statistic 0.6857 0.4174 Log likelihood 1.0787 0.2990 Ratio 5.4 Granger Causality Based on Toda and Yamamoto Procedure JANURAY 2011 VOL 2, NO 9 The Granger causality between core variables (LnNYt, LnHt LnMXt, LnSMXt and LnPXt) based on Toda and Yamamoto (1995) is reported in Table 5. We found evidence of cointegrating between these variables. The causality between under investigated variables must exist either unidirectional or bi-directional because Engle and Granger (1987) and Granger (1988) have shown that if two series are integrated of order 1 I(1), then Granger causality must exist at least in one direction. The optimum lag length of VAR is 2 based on AIC, FPE, HQ, and likelihood ratio criteria. However, all variables are stationary at first difference. This means that dmax = 1 in our case. So, we estimate a system of VAR at levels with a total of k + dmax = 2 + 1 = 3 lags. The results in table 5 suggest that there is unidirectional causality running from manufacturing exports to net income, net income to human capital, semi-manufacturing exports to human capital, semi-manufacturing exports to primary exports and net income to primary exports. And no evidence of bi-directional causality is found between LnNYt, LnHt LnMXt, LnSMXt and LnPXt. COPY RIGHT © 2011 Institute of Interdisciplinary Business Research 457
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