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Model averaging based on James–Stein estimators

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  • Shangwei Zhao

Abstract

Existing model averaging methods are generally based on ordinary least squares (OLS) estimators. However, it is well known that the James–Stein (JS) estimator dominates the OLS estimator under quadratic loss, provided that the dimension of coefficient is larger than two. Thus, we focus on model averaging based on JS estimators instead of OLS estimators. We develop a weight choice method and prove its asymptotic optimality. A simulation experiment shows promising results for the proposed model average estimator. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Shangwei Zhao, 2014. "Model averaging based on James–Stein estimators," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(8), pages 1013-1022, November.
    • Handle: RePEc:spr:metrik:v:77:y:2014:i:8:p:1013-1022
    DOI: 10.1007/s00184-014-0483-y
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    References listed on IDEAS

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    Cited by:

    1. Zhao, Shangwei & Zhou, Jianhong & Li, Hongjun, 2016. "Model averaging with high-dimensional dependent data," Economics Letters, Elsevier, vol. 148(C), pages 68-71.

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