Higgins, Ayden and Jochmans, Koen (2022) Learning Markov Processes with Latent Variables From Longitudinal Data. TSE Working Paper, n. 22-1366

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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: Monograph (Working Paper)
Language: English
Date: 27 September 2022
Uncontrolled Keywords: Dynamic discrete choice, finite mixture, Markov process, regime switching, state dependence
JEL Classification: C14 - Semiparametric and Nonparametric Methods
C23 - Models with Panel Data
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 05 Oct 2022 11:50
Last Modified: 05 Oct 2022 11:50
OAI Identifier: oai:tse-fr.eu:127401
URI: https://publications.ut-capitole.fr/id/eprint/46335
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