DISCRETIZING UNOBSERVED HETEROGENEITY

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, an...

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Bibliographic Details
Published in:Econometrica 2022-03, Vol.90 (2), p.625-643
Main Authors: Bonhomme, Stéphane, Lamadon, Thibaut, Manresa, Elena
Format: Article
Language:English
Subjects:
Classification
Clustering
dimension reduction
Heterogeneity
kmeans clustering
Labor force participation
Labor market
Longitudinal studies
Panel data
Unobserved heterogeneity
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Summary: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.
ISSN:0012-9682
1468-0262
DOI:10.3982/ECTA15238