A non-iteration Bayesian sampling algorithm for robust seemingly unrelated regression models ^*

This paper considers Bayesian analysis of data from seemingly unrelated regression models whose errors have a distribution that is scale mixtures of normal distributions. A non-iterative Bayesian sampling algorithm is developed to obtain the posterior samples, which eliminates the convergence problems in iterative Markov Chain Monte Carlo (MCMC) approach. The performances of the proposed algorithm are illustrated through simulation studies, and the results show that it appears to outperform the MCMC approach and is time-efficient compared to the existing methods. In the case of outliers, the model selection criteria results indicate that the heavy-tailed SUR models is more robust than the normal SUR models. Also, a real data example is analyzed using the proposed algorithm.