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Markov Chain Monte Carlo Analysis of Correlated Count Data

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  • Chib, Siddhartha
  • Winkelmann, Rainer

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.

Suggested Citation

  • Chib, Siddhartha & Winkelmann, Rainer, 2001. "Markov Chain Monte Carlo Analysis of Correlated Count Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 428-435, October.
  • Handle: RePEc:bes:jnlbes:v:19:y:2001:i:4:p:428-35
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