Discretizing Unobserved Heterogeneity

Thibaut Lamadon, Elena Manresa and Stéphane Bonhomme
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Elena Manresa: Massachusetts Institute of Technologhy S

No 1536, 2016 Meeting Papers from Society for Economic Dynamics

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.

Date: 2016
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (14)

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Related works:
Working Paper: Discretizing Unobserved Heterogeneity (2021)
Working Paper: Discretizing Unobserved Heterogeneity (2017)
Working Paper: Discretizing unobserved heterogeneity (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:red:sed016:1536

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