Lag-length Selection in VAR-models Using Equal and Unequal Lag-Length Procedures

It is well known that inference in vector autoregressive models depends crucially on the choice of lag-length. Various lag-length selection procedures have been suggested and evaluated in the literature. In these evaluations the possibility that the true model may have unequal lag-length has, however, received little attention. In this paper we investigate how sensitive lag-length estimation procedures, based on assumptions of equal or unequal lag-lengths, are to the true model structure. The procedures used in the paper are based on information criteria and we give results for AIC, HQ and BIC. In the Monte Carlo study we generate data from a variety of VAR-models with properties similar to macro-economic time-series. We find that the commonly used procedure based on equal lag-length together with AIC and HQ performs well in most cases. The procedure (due to Hsiao) allowing for unequal lag-lengths produce reasonable results when the true model has unequal lag-length. The Hsiao procedure also tend to do better in models with a more complicated lag structure.