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Keywords:

  • AIC;
  • BIC;
  • Kullback–Leibler information;
  • model selection;
  • residual likelihood

Abstract.  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.