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Higgins, Ayden and Jochmans, Koen
(2024)
Learning Markov Processes with Latent Variables.
Econometric Theory.
(In Press)
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Official URL : http://tse-fr.eu/pub/130200
Abstract
We consider the problem of identifying the parameters of a time-homogeneous
bivariate Markov chain when only one of the two variables is observable. We show that, subject to conditions that we spell out, the transition kernel and the distribution of the initial condition are uniquely recoverable (up to an arbitrary relabelling of the state space of the latent variable) from the joint distribution of four (or more) consecutive time-series observations. The result is, therefore, applicable to (short) panel data as well as to (stationary) time series data.
| Item Type: | Article |
|---|---|
| Language: | English |
| Date: | December 2024 |
| 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: | 26 Feb 2025 08:48 |
| OAI Identifier: | oai:tse-fr.eu:130200 |
| URI: | https://publications.ut-capitole.fr/id/eprint/50299 |
