DISCRETIZING UNOBSERVED HETEROGENEITY

• 成果类型: Article
• 署名作者: Bonhomme, Stephane; Lamadon, Thibaut; Manresa, Elena
• 署名单位: University of Chicago; New York University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA15238
• 发表日期: 2022
• 页码: 625-643
• 关键词: nonlinear panel models; bias reduction

摘要

We study discrete panel data methods where unobserved heterogeneity is revealed in a first step, in environments where population heterogeneity is not discrete. We focus on two-step grouped fixed-effects (GFE) estimators, where individuals are first classified into groups using kmeans clustering, and the model is then estimated allowing for group-specific heterogeneity. Our framework relies on two key properties: heterogeneity is a function-possibly nonlinear and time-varying-of a low-dimensional continuous latent type, and informative moments are available for classification. We illustrate the method in a model of wages and labor market participation, and in a probit model with time-varying heterogeneity. We derive asymptotic expansions of two-step GFE estimators as the number of groups grows with the two dimensions of the panel. We propose a data-driven rule for the number of groups, and discuss bias reduction and inference.