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Bayesian Analysis of Multivariate Probit Models

Author

Listed:
  • Siddhartha Chib

    (Washington University)

  • Edward Greenberg

    (Washington University)

Abstract

This paper provides a unified 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 Markov chain Monte Carlo version of the E-M algorithm. Computation of Bayes factors from the simulation output is also considered. The methods are applied to a bivariate data set, to a 534-subject, four-year longitudinal data set from the Six Cities study of the health effects of air pollution, and to a seven-year data set on the labor supply of married women from the Panel Survey of Income Dynamics.

Suggested Citation

  • Siddhartha Chib & Edward Greenberg, 1996. "Bayesian Analysis of Multivariate Probit Models," Econometrics 9608002, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:9608002
    Note: Type of Document - ; to print on PostScript; pages: 24
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    References listed on IDEAS

    as
    1. Avery, Robert B & Hansen, Lars Peter & Hotz, V Joseph, 1983. "Multiperiod Probit Models and Orthogonality Condition Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 24(1), pages 21-35, February.
    2. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Lahiri, Kajal & Gao, Jian, 2002. "Bayesian analysis of nested logit model by Markov chain Monte Carlo," Journal of Econometrics, Elsevier, vol. 111(1), pages 103-133, November.

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    More about this item

    Keywords

    Bayes factors; correlated binary data; Gibbs sampling; marginal likelihood; Markov chain Monte Carlo; Metropolis-Hastings algorithm.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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