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Higgins, Ayden and Jochmans, Koen
(2023)
Identification of mixtures of dynamic discrete choices.
Journal of Econometrics, vol. 237 (n° 1).
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Abstract
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 |
| Subjects: | B- ECONOMIE ET FINANCE |
| 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 |
Available Versions of this Item
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Identification Of Mixtures Of Dynamic Discrete Choices. (deposited 06 Dec 2021 13:46)
- Identification of mixtures of dynamic discrete choices. (deposited 04 May 2023 14:02) [Currently Displayed]
