Identifying Finite Mixtures in Econometric Models

Marc Henry, Yuichi Kitamura () and Bernard Salanié
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Yuichi Kitamura: Cowles Foundation, Yale University, https://economics.yale.edu/people/faculty/yuichi-kitamura

No 1767, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: We consider partial identification of finite mixture models in the presence of an observable source of variation in the mixture weights that leaves component distributions unchanged, as is the case in large classes of econometric models. We first show that when the number J of component distributions is known a priori, the family of mixture models compatible with the data is a subset of a J(J-1)-dimensional space. When the outcome variable is continuous, this subset is defined by linear constraints which we characterize exactly. Our identifying assumption has testable implications which we spell out for J = 2. We also extend our results to the case when the analyst does not know the true number of component distributions, and to models with discrete outcomes.

Keywords: Misclassified regressors; Nonparametric identification (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2010-09, Revised 2013-01
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Quantitative Economics (March 2014), 5(1): 123-144

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