Accounting for model uncertainty in seemingly unrelated regressions Academic Article

abstract

• This article considers inference in a Bayesian seemingly unrelated regression (SUR) model where the set of regressors is assumed unknown a priori. That is, we allow for uncertainty in the covariate set by defining a prior distribution on the model space. The posterior inference is analytically intractable and we adopt computer-intensive simulation using variable dimension Markov chain Monte Carlo algorithms to approximate quantities of interest. Applications are given for vector autoregression (VAR) models of unknown order and multivariate spline models with unknown knot points.

authors

• Mallick, Bani

published proceedings

• JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

author list (cited authors)

• Holmes, C. C., Denison, D., & Mallick, B. K.

citation count

• 27

complete list of authors

• Holmes, CC||Denison, DGT||Mallick, BK

publication date

• January 2002

publisher

• Taylor & Francis  Publisher

published in

• Journal of Computational and Graphical Statistics  Journal

keywords

• Bayesian Model Choice
• Markov Chain Monte Carlo
• Multivariate Regression
• Vector Autoregression

Digital Object Identifier (DOI)

• 10.1198/106186002475

start page

• 533

end page

• 551

volume

• 11

issue

• 3

URL

• http%3A%2F%2Fdx.doi.org%2F10.1198%2F106186002475