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Discretizing Unobserved Heterogeneity

Author

Listed:
  • Thibaut Lamadon

    (University of Chicago)

  • Elena Manresa

    (Massachusetts Institute of Technologhy S)

  • Stephane Bonhomme

    (University of Chicago)

Abstract

We develop two-step and iterative panel data estimators based on a discretization of unobserved heterogeneity. We view discrete estimators as approximations, and study their properties in environments where population heterogeneity is individual-specific and un- restricted, letting the number of types grow with the sample size. Bias reduction methods can improve the performance of discrete estimators. We also show that discrete estimation may strictly dominate fixed-effects approaches when unobservables are high-dimensional, provided their underlying dimension is low. We study two applications: a structural dy- namic discrete choice model of migration, and a model of wage determination with worker and firm heterogeneity. These applications to settings with continuous heterogeneity sug- gest computational and statistical advantages of the discrete methods that we advocate.

Suggested Citation

  • Thibaut Lamadon & Elena Manresa & Stephane Bonhomme, 2016. "Discretizing Unobserved Heterogeneity," 2016 Meeting Papers 1536, Society for Economic Dynamics.
    • Handle: RePEc:red:sed016:1536
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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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