NDLI: Discretizing Unobserved Heterogeneity

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Discretizing Unobserved Heterogeneity

Content Provider	Scilit
Author	Bonhomme, Stéphane Lamadon, Thibaut Manresa, Elena
Copyright Year	2017
Description	Journal: SSRN Electronic Journal We study panel data estimators based on a discretization of unobserved heterogeneity when individual heterogeneity is not necessarily discrete in the population. We focus on two-step grouped-fixed effects estimators, where individuals are classified into groups in a first step using kmeans clustering, and the model is estimated in a second step allowing for group-specific heterogeneity. We analyze the asymptotic properties of these discrete estimators as the number of groups grows with the sample size, and we show that bias reduction techniques can improve their performance. In addition to reducing the number of parameters, grouped fixed-effects methods provide effective regularization. For instance, when allowing for the presence of time-varying unobserved heterogeneity we show they enjoy fast rates of convergence depending on the underlying dimension of heterogeneity. Finally, we document the finite sample properties of two-step grouped fixed-effects estimators in two applications: a structural dynamic discrete choice model of migration, and a model of wages with worker and firm heterogeneity.
Related Links	http://www.ifau.se/globalassets/pdf/se/2017/wp2017-21-discretizing-unobserved-heterogeneity.pdf https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3333452
ISSN	10914358
e-ISSN	15565068
DOI	10.2139/ssrn.3333452
Journal	SSRN Electronic Journal
Language	English
Publisher	Elsevier BV
Publisher Date	2017-04-01
Access Restriction	Open
Subject Keyword	Journal: SSRN Electronic Journal Statistics and Probability Dimension Reduction Panel Data Structural Models Kmeans Clustering
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health Psychiatry and Mental Health

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