A Joint Regression Variable and Autoregressive Order Selection Criterion

Peide Shi and Chih‐Ling Tsai

Journal of Time Series Analysis, 2004, vol. 25, issue 6, 923-941

Abstract: 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.

Date: 2004
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https://doi.org/10.1111/j.1467-9892.2004.00385.x

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