Identifying Latent Structures in Panel Data
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/10.3982/ECTA12560

引用次数
Web of Sciencehttp://aims.cuhk.edu.hk/converis/portal/Publication/0WOS source URL
Scopushttp://aims.cuhk.edu.hk/converis/portal/Publication/1Scopus source URL

其它资讯

摘要This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier-Lasso (C-Lasso) that serves to shrink individual coefficients to the unknown group-specific coefficients. C-Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C-Lasso also achieves the oracle property so that group-specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C-Lasso is preserved in some special cases. Simulations demonstrate good finite-sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.

着者Su L., Shi Z., Phillips P.C.B.
期刊名称Econometrica
出版年份2http://aims.cuhk.edu.hk/converis/portal/Publication/0http://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http://aims.cuhk.edu.hk/converis/portal/Publication/1
卷号84
期次6
出版社Blackwell Publishing Inc.
出版地United Kingdom
页次22http://aims.cuhk.edu.hk/converis/portal/Publication/15 - 2264
国际标準期刊号http://aims.cuhk.edu.hk/converis/portal/Publication/0http://aims.cuhk.edu.hk/converis/portal/Publication/0http://aims.cuhk.edu.hk/converis/portal/Publication/12-9682
语言英式英语

关键词Classification, cluster analysis, dynamic panel, group Lasso, high dimensionality, nonlinear panel, oracle property, panel structure model, parameter heterogeneity, penalized GMM, penalized least squares, penalized profile likelihood