Bayesian Inference in the Seemingly Unrelated Regressions Models
AbstractThe objective of this chapter is to provide a practical guide to computer-aided Bayesian inference for a variety of problems that arise in applications of the SUR model. We describe examples of problems, models and algorithms that have been placed within a general framework in the chapter by Geweke et al (this volume); our chapter can be viewed as complimentary to that chapter. The model is described in Section II; the joint, conditional and marginal posterior density functions that result from a noninformative prior are derived. In Section III we describe how to use sample draws of parameters from their posterior densities to estimate posterior quantities of interest; two Gibbs sampling algorithms and a Metropolis-Hastings algorithm are given.
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Bibliographic InfoPaper provided by The University of Melbourne in its series Department of Economics - Working Papers Series with number 793.
Length: 41 pages
Date of creation: 2001
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