Markov Chain Monte Carlo Analysis of Correlated Count Data

Siddhartha Chib and Rainer Winkelmann

Journal of Business & Economic Statistics, 2001, vol. 19, issue 4, 428-35

Abstract: This article is concerned with the analysis of correlated count data. A class of models is proposed in which the correlation among the counts is represented by correlated latent effects. Special cases of the model are discussed and a tuned and efficient Markov chain Monte Carlo algorithm is developed to estimate the model under both multivariate normal and multivariate-t assumptions on the latent effects. The methods are illustrated with two real data examples of six and sixteen variate correlated counts.

Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:19:y:2001:i:4:p:428-35

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