A Joint Regression Variable and Autoregressive Order Selection Criterion

In linear regression models with autocorrelated errors, we apply the residual likelihood approach to obtain a residual information criterion (RIC), which can jointly select regression variables and autoregressive orders. We show that RIC is a consistent criterion. In addition, our simulation studies indicate that it outperforms heuristic selection criteria - the Akaike information criterion and the Bayesian information criterion - when the signal-to-noise ratio is not weak. Copyright 2004 Blackwell Publishing Ltd.