609 words - 3 pages

The coefficients of Regression 1 (Table 4.1) show the performance of each variable in relation to GDP growth when grouped together. . The estimation results reveal the explanatory variables accounts for a change in approximately 49.6% (R2 value) in economic growth. The coefficient with the best results is GCF at (0.529), followed by education with a negative coefficient of (-1.88). All other variables are highly insignificant. Yet if the estimated effect of GCE were statistically significant, the coefficient of -0.156 of GCE would normally imply that a 1-unit increase in GCE would lead to a fall of 0.15 units. The reason of a negative relationship with growth is due perhaps to the level of corruption factors (Ghosh and Gregoriou 2006). As for Urbanisation would be the best coefficient (2.83) and Trading with the lowest coefficient of (-0.289), these unexpected results are perhaps due to a lack of lags. A valid argument of using GCE, Trade Openness and Urbanisation may require longer lags to be observed rather than what has been incorporated.

“The two-tailed T-test examines the significance of the results. The T-test is made to determine the absolute t-value. If this value lies in between the lower tail and upper tail of the critical value of t, then the null hypothesis that the variables are insignificant is accepted” (Hill, Griffiths, Lim). Table 4.1 shows that all variables are statistically insignificant at the all levels, apart from GCF and education, which one is only significant at 90% and the other 95% level respectively. “The p-value detects whether the impact of the independent variable on the dependant variables is significant. The null hypothesis states that, if the P-value is greater than 0.05, then to reject the null hypothesis that the explanatory variable significantly explains...

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