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

Arnold Zellner, Tomohiro Ando

研究成果: Article査読

41 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)33-45
ページ数13
ジャーナルJournal of Econometrics
159
1
DOI
出版ステータスPublished - 2010 11月 1

ASJC Scopus subject areas

  • 経済学、計量経済学

フィンガープリント

「A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル