Cointegration Test: BOUNDS TEST

F-statistic 16.31489

Test critical values

I(0) I(1)

10% 2.26 3.35

5% 2.62 3.79

2.5% 2.96 4.18

1% 3.41 4.68

Table 14 above shows the test results of cointegration (the existence of a long run relationship) among the variables using the bounds test. I(0) and I(1) are the lower and upper bounds respectively. AS it can be seen from the table, The F-statistic (16.31489) exceeds all the upper bounds at 10 percent, 5 percent, 2.5 percent and 1 percent levels of significance. Thus, the null hypothesis of no long run relationship (no cointegration) is rejected. This means that there exists a long run relationship between the dependent variable (GDP) and the regressors (TOFDIG, TOING, TOINF, TOSE and TOTOT).

3.3.2.3 Long run form

TABLE 15: Long run multipliers (coefficients)

Variable Coefficient Std. Error t-statistic Prob

TOFDIG 0.005927 0.002689 2.204267 0.0362

TOING -0.009809 0.001958 -5.008626 0.0000

TOINF 0.000160 0.000248 0.643807 0.5251

TOSE 0.001887 0.000798 2.365233 0.0255

TOTOT 0.002026 0.000516 3.924483 0.0005

The table above shows the long run regression results of regressing GDP on TOFDIG, TOING, TOINF, TOSECE and TOTOT. As it can be seen from the table, using the probability values in the last column and considering a 5 percent level of significance, TOFDIG, TOSE and TOTOT have a positive significant effect on economic growth in

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the long run. TOINF has a positive insignificant effect on growth in the long run. On the other hand, TOING has a negative significant effects on economic growth in the long run.

3.3.2.4 Short run form

TABLE 16: Short run multipliers (coefficients)

Variable Coefficient Std. Error t-statistic Prob

C 10.20002 1.012378 10.07531 0.0000

D(TOINF) 0.001592 0.000307 5.182295 0.0000

D(TOINF(-1)) 0.000650 0.000255 2.545624 0.0169

D(TOTOT) 0.001608 0.000262 6.140561 0.0000

D(TOTOT(-1)) 0.000738 0.000275 2.687849 0.0122

ECT(-1) -1.163999 0.108067 -10.77112 0.0000

Table 16 above shows the short run regression results of regressing GDP on TOFDIG, TOING, TOINF, TOSE and TOTOT. As it can be seen from the table, using the probability values in the last column and considering a 5 percent level of significance, TOINF and TOTOT have positive significant effects on economic growth in the short run. This is also valid for the previous period (year in this case) TOINF and TOTOT. On the other hand, the Error Correction Term (ECT) is negative and statistically significant.

Its value of -1.163999 means that short run distortions (disequilibrium) are corrected after a year (since annual data was applied) and the path of convergence is oscillatory as opposed to a monotonic path to the long run equilibrium. That is, there is oscillation around the long equilibrium value in a diminishing manner before quickly converging to this value. This confirms the existence of a long run relationship between the dependent variable and the regressors in the model.

TABLE 17: Model 2 Summary Statistics

R-squared 0.808012

Adjusted R-squared 0.778014

F-statistic 26.93540

Prob (F-statistic) 0.000000

Table 17 above shows the summary statistics of the overall model of regressing GDP on TOFDIG, TOING, TOINF, TOSE and TOTOT. As it can be seen from the table, the value of R-squared is 0.808012. This means that under this model, 80.8 percent of the

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fluctuations in the dependent variable (GDP) are explained by the included regressors.

This also means that, only 19.2 percent of the fluctuations in GDP are explained by other factors not included in the model. On the other hand, the value of the adjusted R-squared is 0.778014. This means that 77.8 percent of the fluctuation in GDP are explained by the included regressors and that only 22.2 percent of the fluctuations in GDP are explained by factors not included in the model. Besides, the Prob (F-statistic) value is less than the 5 percent level of significance (that is, less than 0. 05). This means that the overall model is statistically significant. In short, these results show that the model of regressing GDP on TOFDIG, TOING, TOINF, TOSE and TOTOT is a statistically acceptable model.

3.3.2.5 Diagnostic tests

TABLE 18: Results of selected diagnostic tests

Diagnostic Test Prob

Normality of residuals Jarque-Bera 0.758000

Serial correlation in residuals Breusch-Godfrey Serial Correlation LM test 0.2849 Heteroscedasticity in residuals Breusch-Pagan-Godfrey test 0.2287

Model Specification Ramsey RESET test 0.2072

Table 18 above shows the probability values (Prob) of diagnostic tests undertaken in the study to check for the reliability (wellness) of the model for the purpose of estimation/forecasting. Using the Probability values the table above and considering a 5 percent level of significance, decisions were made on the diagnostics under consideration.

In checking for normal distribution in the residuals (errors), normality test using the Jarque-Bera was undertaken testing the null hypothesis of normally distributed residuals against the alternative hypothesis of non-normally distributed residuals. From the results, the null hypothesis was not rejected. Thus, the model does not suffer from the problem of non-normal residuals.

In checking for the presence of serially correlated residuals, the Breusch-Godfrey Serial Correlation LM test was undertaken testing the null hypothesis of no serial correlation in the residuals against the alternative hypothesis of serial correlation in the residuals. From the results, the null hypothesis was not rejected. Thus, the model does not have serially correlated residuals.

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In checking for the presence of heteroscedasticity in the residuals, the Breusch-Pagan-Godfrey test was undertaken. The null hypothesis of homoscedastic residuals (equal variance) was tested against the alternative hypothesis of heteroscedastic residuals (unequal variance). As it can be seen from the table, the probability is greater than 5 percent level of significance. Thus, the null hypothesis was not rejected and the residuals in the model are homoscedastic.

In checking for model specification bias, Ramsey RESET test was undertaken testing the null hypothesis of no model specification bias (no specification error) against the alternative hypothesis of model specification bias (specification error). From the results, the null hypothesis was not rejected and there was no specification bias in setting up this model.

3.3.2.6 Stability tests

Stability tests were undertaken to check for the stability of the regression parameters over the sample period. The CUSUM and CUSUM of squares stability tests were carried out.

77 CUSUM test

FIGURE 23: Parameter stability test

-16 -12 -8 -4 0 4 8 12 16

94 96 98 00 02 04 06 08 10 12 14 16 18

CUSUM 5% Significance

Figure 23 above shows the CUSUM test on parameter stability. As it can be seen from the figure, the blue line does not cross the 5 percent significance bounds. This means that the regression parameters obtained in the study are stable (do not change) over the considered sample period.

78 CUSUM of Squares test

FIGURE 24: Parameter stability test

-0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

94 96 98 00 02 04 06 08 10 12 14 16 18

CUSUM of Squares 5% Significance

Figure 24 above shows the CUSUM of Squares test on parameter stability. As it can be seen from the figure, the blue line does not cross the 5 percent significance bounds.

This means that the regression parameters obtained in the study are stable (do not change) over the considered sample period.

3.4 DISCUSSION OF FINDINGS