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Autoregressive Lag—Order Selection Using Conditional Saddlepoint Approximations

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  • Ronald W. Butler

    (Department of Statistical Science, Southern Methodist University, Dallas, TX 75275-0332, USA)

  • Marc S. Paolella

    (Department of Banking and Finance, University of Zurich, Zurich 8032, Switzerland)

Abstract

A new method for determining the lag order of the autoregressive polynomial in regression models with autocorrelated normal disturbances is proposed. It is based on a sequential testing procedure using conditional saddlepoint approximations and permits the desire for parsimony to be explicitly incorporated, unlike penalty-based model selection methods. Extensive simulation results indicate that the new method is usually competitive with, and often better than, common model selection methods.

Suggested Citation

  • Ronald W. Butler & Marc S. Paolella, 2017. "Autoregressive Lag—Order Selection Using Conditional Saddlepoint Approximations," Econometrics, MDPI, vol. 5(3), pages 1-33, September.
    • Handle: RePEc:gam:jecnmx:v:5:y:2017:i:3:p:43-:d:112377
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    References listed on IDEAS

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