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, Arnold Zellner

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

Computationally efficient simulation methods for hierarchical Bayesian analysis of the seemingly unrelated regression (SUR) and simultaneous equa-tions 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.

Original languageEnglish
Pages (from-to)65-96
Number of pages32
JournalBayesian Analysis
Volume5
Issue number1
DOIs
Publication statusPublished - 2010

Fingerprint

Seemingly Unrelated Regression
Simultaneous Equations Model
Importance sampling
Monte Carlo Sampling
Importance Sampling
Bayesian Analysis
Bayesian Prediction
Predictive Density
Bayesian Estimation
State-space Model
Markov Chain Monte Carlo
Simulation Methods
Moment
Calculate
Interval
Model

Keywords

  • Bayesian estimation and prediction
  • Direct Monte Carlo
  • Hierarchical priors importance sampling
  • Markov Chain Monte Carlo

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability

Cite this

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