Markov Chain Monte Carlo Analysis of Correlated Count Data
Top Cited Papers
- 1 October 2001
- journal article
- research article
- Published by Taylor & Francis in Journal of Business & Economic Statistics
- Vol. 19 (4) , 428-435
- https://doi.org/10.1198/07350010152596673
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.Keywords
This publication has 14 references indexed in Scilit:
- Posterior simulation and Bayes factors in panel count data modelsJournal of Econometrics, 1998
- Debt, moral hazard and airline safety An empirical evidenceJournal of Econometrics, 1997
- DEMAND FOR MEDICAL CARE BY THE ELDERLY: A FINITE MIXTURE APPROACHJournal of Applied Econometrics, 1997
- Markov Chain Monte Carlo Simulation Methods in EconometricsEconometric Theory, 1996
- Understanding the Metropolis-Hastings AlgorithmThe American Statistician, 1995
- Dynamic Count Data Models of Technological InnovationThe Economic Journal, 1995
- Two aspects of labor mobility: A bivariate Poisson regression approachEmpirical Economics, 1993
- The multivariate Poisson-log normal distributionBiometrika, 1989
- A Seemingly Unrelated Poisson Regression ModelSociological Methods & Research, 1989
- Econometric Models for Count Data with an Application to the Patents-R & D RelationshipEconometrica, 1984