Learning Markov Processes with Latent Variables

Koen Jochmans and Ayden Higgins

No 22-1366, TSE Working Papers from Toulouse School of Economics (TSE)

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.

Keywords: Dynamic discrete choice; finite mixture; Markov process; regime switching; state dependence (search for similar items in EconPapers)
JEL-codes: C32 C33 C38 (search for similar items in EconPapers)
Date: 2022-10-04
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.tse-fr.eu/sites/default/files/TSE/docu ... 2022/wp_tse_1366.pdf Full Text (application/pdf)

Related works:
Working Paper: Learning Markov Processes with Latent Variables (2025)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:127401

Access Statistics for this paper

More papers in TSE Working Papers from Toulouse School of Economics (TSE) Contact information at EDIRC.
Bibliographic data for series maintained by ().