Higgins, Ayden and Jochmans, Koen (2023) Identification of mixtures of dynamic discrete choices. Journal of Econometrics, vol. 237 (n° 1).

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Identification Number : 10.1016/j.jeconom.2023.04.006


This paper provides new identification results for finite mixtures of Markov processes. Our arguments yield identification from knowledge of the cross-sectional distribution of three (or more) effective time-series observations under simple conditions. We explain how our approach and results are different from those in previous work by Kasahara and Shimotsu (2009) and Hu and Shum (2012). Most notably, outside information, such as monotonicity restrictions that link conditional distributions to latent types, is not needed.

Item Type: Article
Language: English
Date: November 2023
Refereed: Yes
Place of Publication: Issy-les-Moulineaux
Uncontrolled Keywords: discrete choice, heterogeneity, Markov process, mixture, state dependence
JEL Classification: C14 - Semiparametric and Nonparametric Methods
C23 - Models with Panel Data
C51 - Model Construction and Estimation
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 04 May 2023 14:02
Last Modified: 20 Mar 2024 08:58
OAI Identifier: oai:tse-fr.eu:128057
URI: https://publications.ut-capitole.fr/id/eprint/47805

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