Partial identification of finite mixtures in econometric models

Marc Henry, Yuichi Kitamura, Bernard Salanié

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)123-144
Number of pages22
JournalQuantitative Economics
Volume5
Issue number1
DOIs
StatePublished - Mar 2014

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

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