Dear Amar Amar

you can apply the Wald test for strong causality in a panel ARDL framework, even if your software does not directly support it. Just make sure to double-check your calculations and interpretations to ensure accuracy and validity.

Try Calculate the test statistic manually using the coefficients estimated in your panel ARDL model and the appropriate formula for the Wald test. This may involve using software like Excel or statistical programming languages like R or Python.

Some software packages may not directly support the inclusion of the ECT in the Wald test for strong causality. However, you can still perform the test manually by constructing appropriate test statistics based on the coefficients estimated in your panel ARDL model.

you need to ensure that you're using the appropriate method and including all relevant variables in your model.

Dear Amar Amar

In the panel ARDL framework, the error correction term (ECT) captures the adjustment process towards equilibrium in the long run. Make sure that your model includes the ECT alongside the lagged variables and other relevant explanatory variables.

To estimate strong causality for Panel ARDL (Autoregressive Distributed Lag) models in Eviews, you can follow these steps:

Estimate the Panel ARDL Model: Begin by estimating the Panel ARDL model using Eviews. This involves specifying the appropriate lag order for the variables in your model and estimating the coefficients.

Perform Causality Tests: To assess strong causality in a Panel ARDL framework, you can conduct various tests such as the Wald test, Granger causality test, or the Dumitrescu-Hurlin panel causality test.

Interpretation of Results: After conducting the causality tests, you need to interpret the results to determine if there is evidence of strong causality running from one variable to another in your panel ARDL model.

Wald Test: The Wald test can be used to test for the significance of individual coefficients in the model. If a coefficient is statistically significant, it indicates a strong causal relationship between the corresponding variables.

Granger Causality Test: The Granger causality test helps determine if past values of one variable have a statistically significant effect on another variable in the model. A rejection of the null hypothesis suggests strong causality.

Dumitrescu-Hurlin Panel Causality Test: This test is specifically designed for panel data and can be used to detect causal relationships between variables in a panel ARDL framework.

Consider Cross-Sectional Dependence: When working with panel data, it’s important to account for potential cross-sectional dependence that may exist among individual units in the panel.

Robustness Checks: It is advisable to perform robustness checks to ensure that the results are not sensitive to different specifications or assumptions made in the analysis