Bayesian Analysis

Hierarchical Bayesian analysis of the seemingly unrelated regression and simultaneous equations models using a combination of direct Monte Carlo and importance sampling techniques

Tomohiro Ando and Arnold Zellner

Full-text: Open access

Abstract

Computationally efficient simulation methods for hierarchical Bayesian analysis of the seemingly unrelated regression (SUR) and simultaneous equations models (SEM) are proposed and applied. These methods combine a direct Monte Carlo (DMC) approach and an importance sampling procedure to calculate Bayesian estimation and prediction results, namely, Bayesian posterior densities for parameters, predictive densities for future values of variables and associated moments, intervals and other quantities. The results obtained by our approach are compared to those yielded by use of MCMC techniques. Finally, we show that our algorithm can be applied to the Bayesian analysis of state space models.

Article information

Source
Bayesian Anal., Volume 5, Number 1 (2010), 65-95.

Dates
First available in Project Euclid: 22 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1340369793

Digital Object Identifier
doi:10.1214/10-BA503

Mathematical Reviews number (MathSciNet)
MR2596436

Zentralblatt MATH identifier
1330.62108

Keywords
Bayesian estimation and Prediction Direct Monte Carlo Hierarchical Priors Importance sampling Markov Chain Monte Carlo

Citation

Ando, Tomohiro; Zellner, Arnold. Hierarchical Bayesian analysis of the seemingly unrelated regression and simultaneous equations models using a combination of direct Monte Carlo and importance sampling techniques. Bayesian Anal. 5 (2010), no. 1, 65--95. doi:10.1214/10-BA503. https://projecteuclid.org/euclid.ba/1340369793


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