Bayesian inference for linear models subject to linear inequality constraints

John Geweke

No 552, Working Papers from Federal Reserve Bank of Minneapolis

Abstract: The normal linear model, with sign or other linear inequality constraints on its coefficients, arises very commonly in many scientific applications. Given inequality constraints Bayesian inference is much simpler than classical inference, but standard Bayesian computational methods become impractical when the posterior probability of the inequality constraints (under a diffuse prior) is small. This paper shows how the Gibbs sampling algorithm can provide an alternative, attractive approach to inference subject to linear inequality constraints in this situation, and how the GHK probability simulator may be used to assess the posterior probability of the constraints.

Keywords: Econometric; models (search for similar items in EconPapers)
Date: 1995
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.minneapolisfed.org/research/common/pub_detail.cfm?pb_autonum_id=610 (application/pdf)
http://www.minneapolisfed.org/research/WP/WP552.pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fip:fedmwp:552

Access Statistics for this paper

More papers in Working Papers from Federal Reserve Bank of Minneapolis Contact information at EDIRC.
Bibliographic data for series maintained by Kate Hansel ().