Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices

Authors

  • Hiroyuki Kasahara,

    1. Dept. of Economics, University of Western Ontario, London, Ontario, N6A 5C2 Canada; hkasahar@uwo.ca
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  • Katsumi Shimotsu

    1. Dept. of Economics, Queen's University, 94 University Avenue, Kingston, Ontario, K7L 3N6 Canada; shimotsu@econ.queensu.ca
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    • The authors are grateful to the co-editor and three anonymous referees whose comments greatly improved the paper. The authors thank Victor Aguirregabiria, David Byrne, Seung Hyun Hong, Hidehiko Ichimura, Thierry Magnac, and the seminar participants at Hitotsubashi University, University of Tokyo, University of Toronto, New York Camp Econometrics II, and 2006 JEA Spring Meeting for helpful comments. The financial support from SSHRC is gratefully acknowledged.


Abstract

In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for it. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in applied work under different assumptions on the Markov property, stationarity, and type-invariance in the transition process. Three elements emerge as the important determinants of identification: the time-dimension of panel data, the number of values the covariates can take, and the heterogeneity of the response of different types to changes in the covariates. For example, in a simple case where the transition function is type-invariant, a time-dimension of T = 3 is sufficient for identification, provided that the number of values the covariates can take is no smaller than the number of types and that the changes in the covariates induce sufficiently heterogeneous variations in the choice probabilities across types. Identification is achieved even when state dependence is present if a model is stationary first-order Markovian and the panel has a moderate time-dimension (Tgeqslant R: gt-or-equal, slanted 6).

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