Robust bayesian inference in empirical regression models

Jacek Osiewalski and Mark Steel

UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía

Abstract: Broadening the stochastic assumptions on the error terms of regression models was prompted by the analysis of linear multivariate t models in Zellner (1976). We consider a possible non-linear regression model under any multivariate elliptical data density, and examine Bayesian posterior and productive results. The latter are shown to be robust with respect to the specific choice of a sampling density within this elliptical class. In particular, sufficient conditions for such model robustness are that we single out a precision factor T2 on which we can specify an improper prior density. Apart from the posterior distribution of this nuisance parameter T 2, the entire analysis will then be completely unaffected by departures from Normality. Similar results hold in finite mixtures of such elliptical densities, which can be used to average out specification uncertainty.

Keywords: Multivariate; elliptical; data; densities; Model; robustness; Improper; priors; Finite; mixtures (search for similar items in EconPapers)
Date: 1991-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://e-archivo.uc3m.es/rest/api/core/bitstreams ... 133505f14409/content (application/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:cte:werepe:2814

Access Statistics for this paper

More papers in UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía
Bibliographic data for series maintained by Ana Poveda ().