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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.
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