统计与管理学院2016年学术报告第26期
【主 题】STRUCTURE IDENTIFICATION IN PANEL DATA ANALYSIS
【报告人】 Li Jialiang 教授
新加坡大学
【时 间】 2016年06月08日（星期三）15:00－16:00
【地 点】 上海财经大学统计与管理学院大楼1208室
【摘 要】Panel data analysis is an important topic in statistics and econometrics.
In such analysis, it is very common to assume the impact of a covariate on the response variable remains constant across all individuals. Whilst the modeling based on this assumption is reasonable when only the global effect is of interest, in general, it may overlook some individual/subgroup attributes of the true covariate impact. In this paper, we propose a data driven approach to identify the groups in panel data with interactive effects induced by latent variables. It is assumed that the impact of a covariate is the same within each group, but different between the groups. An EM based algorithm is proposed to estimate the unknown parameters, and a binary segmentation based algorithm is proposed to detect the grouping. We then establish asymptotic theories to justify the proposed estimation, grouping method, and the modeling idea. Simulation studies are also conducted to compare the proposed method with the existing approaches, and the results obtained favour our method. Finally, the proposed method is applied to analyse a data set about income dynamics, which leads to some interesting findings.
【邀请人】 黄涛
