RT Journal Article
SR 00
A1 Higgins, Ayden
A1 Jochmans, Koen
T1 Learning Markov Processes with Latent Variables
JF Econometric Theory
YR 2024
FD 2024-12
K1 dynamic discrete choice
K1 finite mixture
K1 Markov process
K1 regime switching
K1 state dependence
AB 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.
PB Cambridge University press
SN 0266-4666
LK https://publications.ut-capitole.fr/id/eprint/50299/
UL http://tse-fr.eu/pub/130200