Identification Of Mixtures Of Dynamic Discrete Choices

Ayden Higgins and Koen Jochmans

No 21-1272, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: This paper provides new identification results for finite mixtures of Markov processes. Our arguments are constructive and show that identification can be achieved from knowledge of the cross-sectional distribution of three (or more) effective time-series observations under simple conditions. Our approach is contrasted with the ones taken in prior work by Kasahara and Shimotsu (2009) and Hu and Shum (2012). Most notably, monotonicity restrictions that link conditional distributions to latent types are not needed. Maximum likelihood is considered for the purpose of estimation and inference. Implementation via the EM algorithm is straightforward. Its performance is evaluated in a simulation exercise.

Keywords: Discrete choice; heterogeneity; Markov process; mixture; state dependence (search for similar items in EconPapers)
JEL-codes: C14 C23 C51 (search for similar items in EconPapers)
Date: 2021-11-30, Revised 2023-01
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ore
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Identification of mixtures of dynamic discrete choices (2023)
Working Paper: Identification of mixtures of dynamic discrete choices (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:126197

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