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Generalized Cp Model Averaging for Heteroskedastic Models

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  • Liu, Qingfeng

Abstract

This paper proposes a model averaging method, the generalized Mallows’ Cp (GC) method, which works well for heteroskedastic models. Under some regularity conditions, we provide a feasible form of the GC method and show that the GC method has asymptotic optimality not only as a model averaging method but also as a model selection method for heteroskedastic models. We perform some Monte Carlo studies to investigate the small sample properties of the GC method. The simulation results show that our method works well and performs better than alternative methods.

Suggested Citation

  • Liu, Qingfeng, 2011. "Generalized Cp Model Averaging for Heteroskedastic Models," ビジネス創造センターディスカッション・ペーパー (Discussion papers of the Center for Business Creation) 10252/4544, Otaru University of Commerce.
    • Handle: RePEc:ota:busdis:10252/4544
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    References listed on IDEAS

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    1. Wan, Alan T.K. & Zhang, Xinyu & Zou, Guohua, 2010. "Least squares model averaging by Mallows criterion," Journal of Econometrics, Elsevier, vol. 156(2), pages 277-283, June.
    2. Hansen, Bruce E. & Racine, Jeffrey S., 2012. "Jackknife model averaging," Journal of Econometrics, Elsevier, vol. 167(1), pages 38-46.
    3. Hjort N.L. & Claeskens G., 2003. "Frequentist Model Average Estimators," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 879-899, January.
    4. Andrews, Donald W. K., 1991. "Asymptotic optimality of generalized CL, cross-validation, and generalized cross-validation in regression with heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 47(2-3), pages 359-377, February.
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