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.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.