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Lag order selection for an optimal autoregressive covariance matrix estimator

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  • Marco Morales

Abstract

A good parametric spectral estimator requires an accurate estimate of the sum of AR coefficients, however a criterion which minimizes the innovation variance not necessarily yields the best spectral estimate. This paper develops an alternative information criterion considering the bias in the sum of the parameters for the autoregressive estimator of the spectral density at frequency zero.

Suggested Citation

  • Marco Morales, 2010. "Lag order selection for an optimal autoregressive covariance matrix estimator," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 739-748.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:739-748
    DOI: 10.1080/02664760902873969
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    References listed on IDEAS

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    1. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    2. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    3. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-966, July.
    4. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
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    Cited by:

    1. Jie Ding & Vahid Tarokh & Yuhong Yang, 2015. "Bridging AIC and BIC: a new criterion for autoregression," Papers 1508.02473, arXiv.org, revised Aug 2016.

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