Forecasting properties of a new method to determine optimal lag order in stable and unstable VAR models

This simulation study investigates the forecasting performance of a new information criterion suggested by Hatemi-J (2003) to pick the optimal lag length in the stable and unstable vector autregression (VAR) models. The conducted Monte Carlo experiments reveal that this information criterion is successful in selecting the optimal lag order in the VAR model when the main aim is to draw ex-ante (forecasting) inference regardless if the VAR model is stable or not. In addition, the simulations indicate that this information criterion is robust to autoregressive conditional heteroskedasticity effects.