A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model

Arnold Zellner, Tomohiro Ando

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)33-45
Number of pages13
JournalJournal of Econometrics
Volume159
Issue number1
DOIs
Publication statusPublished - 2010 Nov

Fingerprint

Seemingly Unrelated Regression
Bayesian Analysis
Regression Model
Markov processes
Sales
Bayesian Prediction
Bayesian Estimation
Markov Chain Monte Carlo
Moment
Calculate
Interval
Regression model
Seemingly unrelated regression
Bayesian analysis

Keywords

  • Bayesian Monte Carlo techniques
  • Bayesian multivariate analysis
  • Direct MC methods
  • MCMC

ASJC Scopus subject areas

  • Economics and Econometrics
  • Applied Mathematics
  • History and Philosophy of Science

Cite this

A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model. / Zellner, Arnold; Ando, Tomohiro.

In: Journal of Econometrics, Vol. 159, No. 1, 11.2010, p. 33-45.

Research output: Contribution to journalArticle

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