Analysis of multivariate probit models

1998/06/01 by Siddhartha Chib · 7 citations
Mathematics · Economics, Econometrics and Finance · #Statistical Methods and Bayesian Inference #Economic and Environmental Valuation #Spatial and Panel Data Analysis

paper · doi:10.1093/biomet/85.2.347

openalex publication_date 1998/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

This paper provides a practical simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods and maximum likelihood estimates are obtained by a Monte Carlo version of the EM algorithm. A practical approach for the computation of Bayes factors from the simulation output is also developed. The methods are applied to a dataset with a bivariate binary response, to a four-year longitudinal dataset from the Six Cities study of the health effects of air pollution and to a sevenvariate binary response dataset on the labour supply of married women from the Panel survey of Income Dynamics.

Citations

• The Shape of Correlation Matrices
• Understanding the Metropolis-Hastings Algorithm
• Bayesian Analysis of Binary and Polychotomous Response Data
• Monte Carlo sampling methods using Markov chains and their applications

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