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Higgins, Ayden and Jochmans, Koen
(2021)
Identification Of Mixtures Of Dynamic Discrete Choices.
TSE Working Paper, n. 21-1272, Toulouse
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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.
| Item Type: | Monograph (Working Paper) |
|---|---|
| Language: | English |
| Date: | 23 November 2021 |
| Place of Publication: | Toulouse |
| Additional Information: | Est en bash edit, mais il y a quand même l'accès pour le modifier et le valider. Que faire ? |
| 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) |
| Institution: | Université Toulouse 1 Capitole |
| Site: | UT1 |
| Date Deposited: | 06 Dec 2021 13:46 |
| Last Modified: | 20 Oct 2023 14:13 |
| OAI Identifier: | oai:tse-fr.eu:126197 |
| URI: | https://publications.ut-capitole.fr/id/eprint/44015 |
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