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Model averaging based on generalized method of moments
Wang, Weiwei1,2; Zhang, Qi1; Zhang, Xinyu3; Li, Xinmin1
2021-03-01
发表期刊ECONOMICS LETTERS
ISSN0165-1765
卷号200页码:5
摘要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. (c) 2021 Elsevier B.V. All rights reserved.
关键词Generalized method of moments Model averaging Cross-validation criterion Asymptotic optimality
DOI10.1016/j.econlet.2021.109735
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[11871294] ; National Natural Science Foundation of China[72042019] ; National Natural Science Foundation of China[72091212]
WOS研究方向Business & Economics
WOS类目Economics
WOS记录号WOS:000623053700005
出版者ELSEVIER SCIENCE SA
引用统计
文献类型期刊论文
条目标识符http://ir.amss.ac.cn/handle/2S8OKBNM/58219
专题中国科学院数学与系统科学研究院
通讯作者Li, Xinmin
作者单位1.Qingdao Univ, Sch Math & Stat, Qingdao 266071, Peoples R China
2.East China Normal Univ, Sch Stat, Shanghai 200062, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Wang, Weiwei,Zhang, Qi,Zhang, Xinyu,et al. Model averaging based on generalized method of moments[J]. ECONOMICS LETTERS,2021,200:5.
APA Wang, Weiwei,Zhang, Qi,Zhang, Xinyu,&Li, Xinmin.(2021).Model averaging based on generalized method of moments.ECONOMICS LETTERS,200,5.
MLA Wang, Weiwei,et al."Model averaging based on generalized method of moments".ECONOMICS LETTERS 200(2021):5.
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