Discretizing Unobserved Heterogeneity

Stéphane Bonhomme, Thibaut Lamadon, Elena Manresa

    Research output: Contribution to journalArticlepeer-review


    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.

    Original languageEnglish (US)
    Pages (from-to)625-643
    Number of pages19
    Issue number2
    StatePublished - Mar 2022


    • Unobserved heterogeneity
    • dimension reduction
    • kmeans clustering
    • panel data

    ASJC Scopus subject areas

    • Economics and Econometrics


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