On the estimation of partially observed continuous-time Markov chains

Alan Riva-Palacio (), Ramsés H. Mena () and Stephen G. Walker ()
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Alan Riva-Palacio: IIMAS, UNAM
Ramsés H. Mena: IIMAS, UNAM
Stephen G. Walker: University of Texas at Austin

Computational Statistics, 2023, vol. 38, issue 3, No 12, 1357-1389

Abstract: Abstract Motivated by the increasing use of discrete-state Markov processes across applied disciplines, a Metropolis–Hastings sampling algorithm is proposed for a partially observed process. Current approaches, both classical and Bayesian, have relied on imputing the missing parts of the process and working with a complete likelihood. However, from a Bayesian perspective, the use of latent variables is not necessary and exploiting the observed likelihood function, combined with a suitable Markov chain Monte Carlo method, results in an accurate and efficient approach. A comprehensive comparison with simulated and real data sets demonstrate our approach when compared with alternatives available in the literature.

Keywords: Bayesian estimation; Transition matrix; Credit risk scoring (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00180-022-01273-w

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