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A Joint Regression Variable and Autoregressive Order Selection Criterion

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  • Peide Shi
  • Chih‐Ling Tsai

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.

Suggested Citation

  • Peide Shi & Chih‐Ling Tsai, 2004. "A Joint Regression Variable and Autoregressive Order Selection Criterion," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 923-941, November.
    • Handle: RePEc:bla:jtsera:v:25:y:2004:i:6:p:923-941
    DOI: 10.1111/j.1467-9892.2004.00385.x
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    References listed on IDEAS

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    1. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    2. Zinde-Walsh, Victoria & Galbraith, John W., 1991. "Estimation of a linear regression model with stationary ARMA(p, q) errors," Journal of Econometrics, Elsevier, vol. 47(2-3), pages 333-357, February.
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    Cited by:

    1. Søren Johansen & Marco Riani & Anthony C. Atkinson, 2012. "The Selection of ARIMA Models with or without Regressors," Discussion Papers 12-17, University of Copenhagen. Department of Economics.
    2. Yun-Huan Lee & Chun-Shu Chen, 2012. "Autoregressive model selection based on a prediction perspective," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 913-922, October.
    3. Arslan, Olcay, 2012. "Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1952-1965.

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