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/10.1080/07474938.2015.1092805 |
引用次数
Web of Sciencehttp://aims.cuhk.edu.hk/converis/portal/Publication/1WOS source URL
Scopushttp://aims.cuhk.edu.hk/converis/portal/Publication/1Scopus source URL
其它资讯
摘要We apply the generalized method of moments–least absolute shinkage and selection operator (GMM-Lasso) (Caner, 2009) to a linear structural model with many endogenous regressors. If the true parameter is sufficiently sparse, we can establish a new oracle inequality, which implies that GMM-Lasso performs almost as well as if we knew a priori the identities of the relevant variables. Sparsity, meaning that most of the true coefficients are too small to matter, naturally arises in econometric applications where the model can be derived from economic theory. In addition, we propose to use a modified version of AIC or BIC to select the tuning parameter in practical implementation. Simulations provide supportive evidence concerning the finite sample properties of the GMM-Lasso.
着者Shi Z.
期刊名称Econometric Reviews
出版年份20http://aims.cuhk.edu.hk/converis/portal/Publication/16
月份http://aims.cuhk.edu.hk/converis/portal/Publication/1http://aims.cuhk.edu.hk/converis/portal/Publication/1
日期25
卷号35
期次8-http://aims.cuhk.edu.hk/converis/portal/Publication/10
出版社Marcel Dekker Inc.
出版地United States
页次http://aims.cuhk.edu.hk/converis/portal/Publication/1582 - http://aims.cuhk.edu.hk/converis/portal/Publication/1608
国际标準期刊号0747-4938
语言英式英语
关键词Big data, Endogeneity, GMM, High-dimensional, Sparsity
