Data Augmentation in the Bayesian Multivariate Probit Model

Roberto Leon-Gonzalez

No 2004001, Working Papers from The University of Sheffield, Department of Economics

Abstract: This paper is concerned with the Bayesian estimation of a Multivariate Probit model. In particular, this paper provides an algorithm that obtains draws with low correlation much faster than a pure Gibbs sampling algorithm. The algorithm consists in sampling some characteristics of slope and variance parameters marginally on the latent data. Estimations with simulated datasets illustrate that the proposed algorithm can be much faster than a pure Gibbs sampling algorithm. For some datasets, the algorithm is also much faster than the e±cient algorithm proposed by Liu and Wu (1999) in the context of the univariate Probit model.

Pages: 15 pages
Date: 2004-01, Revised 2004-01
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:shf:wpaper:2004001

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