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Sampling the Variance-Covariance Matrix\ud in the Bayesian Multivariate Probit Model

By R. Leon-Gonzalez


This paper is concerned with the Bayesian estimation of a Multivariate Probit\ud model. In particular, this paper provides a method to sample the restricted variancecovariance\ud matrix directly from its conditional posterior density. The method allows\ud the application of a standard Gibbs sampling algorithm to sample from the posterior\ud density of the parameters, and hence it avoids the use of a Metropolis step. The method\ud uses a decomposition of the Inverted Wishart density and alternative identification\ud restrictions.\u

Publisher: Department of Economics, University of Sheffield
Year: 2003
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