Title

Identifying Finite Mixtures in Econometric Models

Document Type

Discussion Paper

Publication Date

9-1-2010

CFDP Number

1767

CFDP Revision Date

2013-01-01

CFDP Pages

32

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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