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A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model. (English) Zbl 1431.62295
Summary: Computationally efficient methods for Bayesian analysis of seemingly unrelated regression (SUR) models are described and applied that involve the use of a direct Monte Carlo (DMC) approach to calculate Bayesian estimation and prediction results using diffuse or informative priors. This DMC approach is employed to compute Bayesian marginal posterior densities, moments, intervals and other quantities, using data simulated from known models and also using data from an empirical example involving firms’ sales. The results obtained by the DMC approach are compared to those yielded by the use of a Markov Chain Monte Carlo (MCMC) approach. It is concluded from these comparisons that the DMC approach is worthwhile and applicable to many SUR and other problems.

MSC:
62J05 Linear regression; mixed models
62-08 Computational methods for problems pertaining to statistics
62F15 Bayesian inference
62P20 Applications of statistics to economics
65C40 Numerical analysis or methods applied to Markov chains
Software:
bayesm
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