1) Model Specification: VAR models typically assume that all variables are stationary in the same order of integration. When you have a mix of variables stationary at levels and differences, this violates the stationarity assumption of the VAR model. As a result, the model specification is not consistent with the underlying data-generating process, potentially leading to biased parameter estimates and inefficient inference.

2) Interpretation of Coefficients: Interpreting coefficients in a panel VAR with mixed integration orders can be challenging. Coefficients on differenced variables represent short-run dynamic relationships, while coefficients on stationary variables in levels capture long-run equilibrium relationships. Therefore, you need to be cautious when interpreting the coefficients, as they may reflect different economic phenomena.

3) Granger Causality: Testing for Granger causality becomes more complex when dealing with panel VARs with mixed integration orders. Granger causality tests assume that the variables are stationary and that the model is correctly specified. Therefore, you may need to use modified Granger causality tests that account for mixed integration orders, such as the Toda-Yamamoto procedure or panel causality tests specifically designed for heterogeneous integration orders.

References

Thank You sir for your reply. I will look into this!