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

Flavia Barsotti, Yohann De Castro, Thibault Espinasse and Paul Rochet

Statistics & Probability Letters, 2014, vol. 94, issue C, 98-105

Abstract: 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.

Keywords: Time varying Markov process; Identifiability; Sparse transition matrix; Parametric estimation; Asymptotic normality; Lie bracket (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1016/j.spl.2014.07.009

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