Model averaging by jackknife criterion in models with dependent data

作     者：Zhang, Xinyu Wan, Alan T. K. Zou, Guohua 

作者机构：Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China City Univ Hong Kong Dept Management Sci Kowloon Hong Kong Peoples R China 

出 版 物：《JOURNAL OF ECONOMETRICS》 (经济计量学杂志)

年 卷 期：2013年第174卷第2期

页      面：82-94页

核心收录：

学科分类：02[经济学] 0201[经济学-理论经济学] 0701[理学-数学] 

基　　金：National Natural Science Foundation of China [71101141, 70933003, 70625004, 11021161] Science Foundation of the Chinese Academy of Sciences NCMIS General Research Fund from the Hong Kong Research Grants Council [CityU-102709] Hundred Talents Program of the Chinese Academy of Sciences 

主　　题：Asymptotic optimality Autocorrelation Cross-validation Lagged dependent variables Model averaging Squared error 

摘      要：The past decade witnessed a literature on model averaging by frequentist methods. For the most part, the asymptotic optimality of various existing frequentist model averaging estimators has been established under i.i.d. errors. Recently, Hansen and Racine [Hansen, B.E., Racine, J., 2012. jackknife model averaging. Journal of Econometrics 167, 38-46] developed a jackknife model averaging (JMA) estimator, which has an important advantage over its competitors in that it achieves the lowest possible asymptotic squared error under heteroscedastic errors. In this paper, we broaden Hansen and Racine s scope of analysis to encompass models with (i) a non-diagonal error covariance structure, and (ii) lagged dependent variables, thus allowing for dependent data. We show that under these set-ups, the JMA estimator is asymptotically optimal by a criterion equivalent to that used by Hansen and Racine. A Monte Carlo study demonstrates the finite sample performance of the JMA estimator in a variety of model settings. (C) 2013 Elsevier B.V. All rights reserved.