Prediction for Seemingly Unrelated Regressions
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 1992 · Volume 54 · Issue 1
DOI: 10.1111/j.2517-6161.1992.tb01878.x
Abstract
The seemingly unrelated regressions model is described and analysed from a Bayesian perspective. The predictive density for this model cannot, in general, be evaluated analytically. Two approximations are proposed and investigated. These are Gibbs sampling and a first-order approximation based on a Bayes estimate of the precision matrix. These approximations are then compared on simulated data and both appear to give good results. Extensions to allow for missing data are also discussed.
Keywords
bayes estimate, gibbs sampling, predictive density function, seemingly unrelated regressions
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