Large-Scale Generalized Linear Models for Longitudinal Data with Grouped Patterns of Unobserved Heterogeneity

作     者：Ando, Tomohiro Bai, Jushan 

作者机构：Univ Melbourne Melbourne Business Sch 200 Leicester St Carlton Vic 3053 Australia Columbia Univ Dept Econ New York NY 10027 USA 

出 版 物：《JOURNAL OF BUSINESS & ECONOMIC STATISTICS》 (商业与经济统计学杂志)

年 卷 期：2023年第41卷第3期

页      面：983-994页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 0201[经济学-理论经济学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 

基　　金：China Meeting of the Econometric Society 

主　　题：Clustering Factor analysis Generalized linear models Interactive fixed effects Longitudinal data Unobserved heterogeneity 

摘      要：This article provides methods for flexibly capturing unobservable heterogeneity from longitudinal data in the context of an exponential family of distributions. The group memberships of individual units are left unspecified, and their heterogeneity is influenced by group-specific unobservable factor structures. The model includes, as special cases, probit, logit, and Poisson regressions with interactive fixed effects along with unknown group membership. We discuss a computationally efficient estimation method and derive the corresponding asymptotic theory. Uniform consistency of the estimated group membership is established. To test heterogeneous regression coefficients within groups, we propose a Swamy-type test that allows for unobserved heterogeneity. We apply the proposed method to the study of market structure of the taxi industry in New York City. Our method unveils interesting and important insights from large-scale longitudinal data that consist of over 450 million data points.