Gibbs Samplers for a Set of Seemingly Unrelated Regressions

Bayesian estimation of a collection of seemingly unrelated regressions, referred to as a ‘set of seemingly unrelated regressions’ is considered. The collection of seemingly unrelated regressions is linked by common coefficients and/or a common error covariance matrix. Gibbs samplers useful for estimating posterior quantities are described and applied to two examples – a set of linear expenditure functions and a cost function and share equations from production theory.