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Model averaging based on generalized method of moments

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  • Wang, Weiwei
  • Zhang, Qi
  • Zhang, Xinyu
  • Li, Xinmin

Abstract

Model averaging has become a hot topic in the field of statistics and econometrics with its robustness. Existing model averaging methods are based on the linear estimation mainly, but the research of model averaging based on the generalized method of moments(GMM) still has a lot of works to be done. In this paper, we consider using model average method to estimate a GMM based regression model. The weights are obtained by minimizing the J-fold cross validation criterion, and the asymptotic optimality of the model averaging estimator in the sense that it minimizes the squared estimation loss is proved. The simulation study shows that the risks of our GMM model averaging estimators are relatively low in some cases.

Suggested Citation

  • Wang, Weiwei & Zhang, Qi & Zhang, Xinyu & Li, Xinmin, 2021. "Model averaging based on generalized method of moments," Economics Letters, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:ecolet:v:200:y:2021:i:c:s0165176521000124
    DOI: 10.1016/j.econlet.2021.109735
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    References listed on IDEAS

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    2. Chen, Xingyi & Li, Haiqi & Zhang, Jing, 2023. "Complete subset averaging approach for high-dimensional generalized linear models," Economics Letters, Elsevier, vol. 226(C).

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