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Table 3: Cointegration Result/ Engel-Granger Two Step Since the variables in the model are non-stationary but cointegrated by Engel-Granger technique, the study therefore estimated both the static and dynamic model. The dynamic model shows similar behavior as the static model both models arre reported below.

Table 3 Cointegration Result/ Engel-Granger Two Step Since the variables in the model are non-stationary but cointegrated by Engel-Granger technique, the study therefore estimated both the static and dynamic model. The dynamic model shows similar behavior as the static model both models arre reported below.

Related Figures (6)

The growth hypothesis = where causality runs from energy consumption to economic growth; The conservation hypothesis = where causality runs from economic growth to energy consumption; The neutrality hypothesis = both economic growth and energy consumption has no effect to each other; The feedback hypothesis = both economic growth and energy consumption granger cause each other Research and Methodology
The growth hypothesis = where causality runs from energy consumption to economic growth; The conservation hypothesis = where causality runs from economic growth to energy consumption; The neutrality hypothesis = both economic growth and energy consumption has no effect to each other; The feedback hypothesis = both economic growth and energy consumption granger cause each other Research and Methodology
Source: Authors Computations from E-Views 9.0 Results Table 4: Results from the Static Model
Source: Authors Computations from E-Views 9.0 Results Table 4: Results from the Static Model
Table 5: Dynamic Model
Table 5: Dynamic Model
The result in Table 6 shows the acceptance of the null hypothesis of the four assumptions diagnosed. That is the residuals from the model are normally distributed with a constant mean and variance and are not autocorrelated since the probability values of the test statistics are not significant at the 5 percent level. Also, it was revealed that the model was correctly specified which indicates that there is no specification bias. Figure | and 2 are aimed at examining whether any of the estimated coefficients falls outside the cycles. If any of the coefficients falls outside the circle then, the coefficient estimated is not stable. The results from figure | and 2 show that none of the coefficient falls outside the CUSUM and CUSUM square circle at 5% level, hence the parameters estimated are stable.
The result in Table 6 shows the acceptance of the null hypothesis of the four assumptions diagnosed. That is the residuals from the model are normally distributed with a constant mean and variance and are not autocorrelated since the probability values of the test statistics are not significant at the 5 percent level. Also, it was revealed that the model was correctly specified which indicates that there is no specification bias. Figure | and 2 are aimed at examining whether any of the estimated coefficients falls outside the cycles. If any of the coefficients falls outside the circle then, the coefficient estimated is not stable. The results from figure | and 2 show that none of the coefficient falls outside the CUSUM and CUSUM square circle at 5% level, hence the parameters estimated are stable.
Table 6: Summary of Diagnostic Test
Table 6: Summary of Diagnostic Test
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