Estimating the transition matrix of a Markov chain observed at random times

We want to recover the transition kernel P of a Markov chain X when only a sub-sequence of X is available. The time gaps between the observations are iid with unknown distribution. We propose a method to build an estimator of P under the assumption that it has some zero entries. Its asymptotic performance is discussed in theory and through numerical simulations.

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Year of publication:	2014
Authors:	Barsotti, Flavia ; De Castro, Yohann ; Espinasse, Thibault ; Rochet, Paul
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 94.2014, C, p. 98-105
Publisher:	Elsevier
Subject:	Time varying Markov process \| Identifiability \| Sparse transition matrix \| Parametric estimation \| Asymptotic normality \| Lie bracket

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010930576