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Generalized Cp Model Averaging for Heteroskedastic Models

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  • Liu, Qingfeng

Abstract

This paper proposed a model averaging method, which is called Generalized Mallows’ Cp model averaging (GC). It works well for heteroskedastic models. Under some regularity conditions, we show that our GC has asymptotic optimality as a model averaging method, and also has asymptotic optimality as a model selection method as well for heteroskedastic model. Some Monte-Carlo studies are performed to investigate the small sample properties of GC. The simulation results show that our method works well, gives better performance than other alternative methods.

Suggested Citation

  • Liu, Qingfeng, 2010. "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) 10252/4334, Otaru University of Commerce.
    • Handle: RePEc:ota:busdis:10252/4334
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    References listed on IDEAS

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    1. 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.
    2. 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.
    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. Hansen, Bruce E. & Racine, Jeffrey S., 2012. "Jackknife model averaging," Journal of Econometrics, Elsevier, vol. 167(1), pages 38-46.
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