For

checking that whether the results are economic significance and reasonable. The

estimated model is passed through diagnostic tests of the econometrics to check

its consistency and model is checked the stability of the model. Result of

Table 6 shows the Diagnostic tests: serial correlation, functional form,

normality, heteroscedasticity, and structural stability.

It

is found that there is no correlation i.e., The critical values for the test

are dL = 0.88, dU = 1.61, so 4 ? dU = 3.12 and 4 ? dL = 2.39. As the test

statistic (1.870818) lies between the upper (1.61) and 4 ? dU = 3.12, therefore,

hence the null hypothesis of no autocorrelation is not rejected and it would be

concluded that the residuals from the model appear to be not autocorrelated.

Therefore, the analysis satisfies the assumption of independent of errors. The model

of functional form is matched with test of Ramsey’s RESET.

As Bera-Jarque statistic p-value is 0.72 which

is greater than 0.05, therefore, do not reject the null hypothesis and it is

concluded that the residual values are normally distributed.

As

P-value for the F-statistic value which is 0.58 exceeds 0.05 level of

significance, therefore, it is concluded that there is no heteroscedasticity in

the variance of the residuals. In the presence of heteroscedasticity, a

remedial measure can be taken to rectify this situation by using the robust

standard errors option for correcting the (conventional) standard errors.

Finally,

as all Diagnostic test have model passes all of the reported diagnostic tests. it can be say that results of study are economically

significant and reasonable because the estimated model of study have passed all

the diagnostic tests.

5.5. Test For Structure Stabiltiy Of

Model

With

the help of cumulative sum of recursive residuals (CUSUM) and cumulative sum of

squares of recursive residuals (CUSUMSQ) structural stability of model are checked.

The concept of stability testing technique was first presented by Brown et al.

(1975), the stability of model of error correction model selected by ARDL can

be test with CUSUM and CUSUMSQ plots which are shown in Figures 5.1 and 5.2

respectively. According to both the plots, the plots are within critical bounds

at 5 percent level of significance, Therefore, they confirm the long-run

relationships among variables and the stability of the model.