Bayesian and non-Bayesian analysis of the seemingly unrelated regression model with Student-t errors, and its application for forecasting

Arnold Zellner, Tomohiro Ando

研究成果: Article査読

16 被引用数 (Scopus)

抄録

A description of computationally efficient methods for the Bayesian analysis of Student-t seemingly unrelated regression (SUR) models with unknown degrees of freedom is given. The method combines a direct Monte Carlo (DMC) approach with an importance sampling procedure to calculate Bayesian estimation and prediction results using a diffuse prior. This approach is employed to compute Bayesian posterior densities for the parameters, as well as predictive densities for future values of variables and the associated moments, intervals and other quantities that are useful to both forecasters and others. The results obtained using our approach are compared to those yielded by the use of DMC for a standard normal SUR model.

本文言語English
ページ(範囲)413-434
ページ数22
ジャーナルInternational Journal of Forecasting
26
2
DOI
出版ステータスPublished - 2010 4月

ASJC Scopus subject areas

  • ビジネスおよび国際経営

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