Learning markov processes with latent variables

Ayden Higgins () and Koen Jochmans
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Ayden Higgins: University of Exeter Business School - University of Exeter

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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.

Keywords: dynamic discrete choice; finite mixture; Markov process; regime switching; state dependence (search for similar items in EconPapers)
Date: 2026
Note: View the original document on HAL open archive server: https://hal.science/hal-05488665v1
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Published in Econometric Theory, 2026, pp.1-13. ⟨10.1017/S0266466625000027⟩

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Related works:
Working Paper: Learning Markov Processes with Latent Variables (2025)
Working Paper: Learning Markov Processes with Latent Variables (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05488665

DOI: 10.1017/S0266466625000027

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