Publication in refereed journal
香港中文大学研究人员 ( 现职)
| 史震涛教授 (经济学系) |
全文
| 数位物件识别号 (DOI) http://dx.doi.org/10.1016/j.jeconom.2016.07.004 |
引用次数
Web of Sciencehttp://aims.cuhk.edu.hk/converis/portal/Publication/0WOS source URL
其它资讯
摘要We consider a nonlinear structural model in which the number of moments is not limited by the sample size. The econometric problem here is to estimate and perform inference on a nite-dimensional parameter. To handle the high dimensionality, we must systematically choose a set of informative moments; in other words, delete the uninformative ones. In nonlinear models, a consistent estimator is a prerequisite for moment selection. We develop in this paper a novel two-step procedure. The rst step achieves consistency in high-dimensional asymptotics by relaxing the moment constraints of empirical likelihood. Given the consistent estimator, in the second step we propose a computationally ecient algorithm to select the informative moments from a vast number of candidate combinations, and then use these moments to correct the bias of the rst-step estimator. We show that the resulting second-step estimator is √ n-asymptotic normal, and achieves the lowest variance under a sparsity condition. To the best of our knowledge, we provide the rst asymptotically normally distributed estimator in such an environment. The new estimator is shown to have favorable nite sample properties in simulations, and it is applied to estimate an international trade model with massive Chinese datasets.
出版社接受日期15.http://aims.cuhk.edu.hk/converis/portal/Publication/09.2http://aims.cuhk.edu.hk/converis/portal/Publication/016
着者Shi Z
期刊名称Journal of Econometrics
出版年份2http://aims.cuhk.edu.hk/converis/portal/Publication/016
月份11
卷号195
期次1
出版社Elsevier
页次1http://aims.cuhk.edu.hk/converis/portal/Publication/04 - 119
国际标準期刊号http://aims.cuhk.edu.hk/converis/portal/Publication/03http://aims.cuhk.edu.hk/converis/portal/Publication/04-4http://aims.cuhk.edu.hk/converis/portal/Publication/076
电子国际标準期刊号1872-6895
语言美式英语
