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Identifying Finite Mixtures in Econometric Models

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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.

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File URL: http://cowles.econ.yale.edu/P/cd/d17b/d1767.pdf
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Bibliographic Info

Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1767.

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Length: 32 pages
Date of creation: Sep 2010
Date of revision: Jan 2013
Handle: RePEc:cwl:cwldpp:1767

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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

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Keywords: Misclassified regressors; Nonparametric identification;

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Cited by:
  1. Spindler, Martin, 2013. "“They do know what they are doing... at least most of them.” Asymmetric Information in the (private) Disability Insurance," MEA discussion paper series 12260, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
  2. Stefan Hoderlein & Lars Nesheim & Anna Simoni, 2012. "Semiparametric estimation of random coefficients in structural economic models," CeMMAP working papers CWP09/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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