*3.5.2 Heteroscedasticity test*

The Breusch-Pagan-Godfrey tests for heteroscedasticity statistic for the null hypothesis of no heteroscedasticity in regressions I and II have probability values of 0.6996 and 0.0612, respectively, which are greater than 5 percent. Thus, we fail to reject the null hypothesis, which indicates that there is no heteroscedasticity in the residuals (**Table 5**).


#### **Table 4.**

*The Breusch-Godfrey test for serial correlation in the residuals of the regression.*


#### **Table 5.**

*Breusch-Pagan-Godfrey test for heteroscedasticity results.*

## *3.5.3 Normality test*

The ARDL model assumes that the residuals are normally distributed. The Jarque-Bera statistic is assumed to have a *chi-square* (χ2) distribution with two degrees of freedom, and the null hypothesis assumes that the errors are normally distributed [92–94].

As indicated in **Figure 3**, in regression I, the probability value for the Jarque-Bera statistic is 0.49 with a probability value of 0.782, which is more than 5 percent; hence, the residuals are normally distributed. In regression II, the probability value for the Jarque-Bera statistic is 0.647 with a probability value of 0.724, which is more than 5 percent; hence, the residuals are normally distributed. This means that statistical tests for inference on regression coefficients are reliable, since these tests require that the dependent variable (and hence the residuals) follows a normal distribution.
