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Higgins, Ayden and Jochmans, Koen
(2025)
Learning Markov Processes with Latent Variables.
Econometric Theory.
(In Press)
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Abstract
We present a constructive proof of (nonparametric) identication of the parameters of a bivariate Markov chain when only one of the two random variables is observable. This setup generalizes the hidden Markov model in various useful directions, allowing for state dependence in the observables and allowing the transition kernel of the hidden Markov chain to depend on past observables. We give conditions under which the transition kernel and the distribution of the initial condition are both identied (up to a permutation of the latent states) from the joint distribution of four (or more) time-series observations.
| Item Type: | Article |
|---|---|
| Language: | English |
| Date: | 2025 |
| Refereed: | Yes |
| Place of Publication: | Cambridge |
| Uncontrolled Keywords: | dynamic discrete choice, finite mixture, Markov process, regime switching, state dependence |
| JEL Classification: | C32 - Time-Series Models C33 - Models with Panel Data |
| Subjects: | B- ECONOMIE ET FINANCE |
| Divisions: | TSE-R (Toulouse) |
| Site: | UT1 |
| Date Deposited: | 31 Jan 2025 10:06 |
| Last Modified: | 06 May 2025 08:54 |
| OAI Identifier: | oai:tse-fr.eu:130200 |
| URI: | https://publications.ut-capitole.fr/id/eprint/50299 |
