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Model selection of M-estimation models using least squares approximation

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  • Mao, Guangyu

Abstract

This paper proposes a new criterion for the M-estimation models based on a least squares approximation, which is proved to be selection consistent. Compared with the existing criteria, this new one has two attractive features. One is that model selection based on it has much lower computational cost. The other is that it may bring considerable improvement in some cases since it is essentially based on the efficient GMM estimation.

Suggested Citation

  • Mao, Guangyu, 2015. "Model selection of M-estimation models using least squares approximation," Statistics & Probability Letters, Elsevier, vol. 99(C), pages 238-243.
    • Handle: RePEc:eee:stapro:v:99:y:2015:i:c:p:238-243
    DOI: 10.1016/j.spl.2015.01.027
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    References listed on IDEAS

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    7. Mao, Guangyu, 2013. "Model selection for regression with heteroskedastic and autocorrelated errors," Economics Letters, Elsevier, vol. 118(3), pages 497-501.
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