Bayesian Semi-nonparametric ARCH Models
Gary Koop
The Review of Economics and Statistics, 1994, vol. 76, issue 1, 176-81
Abstract:
A Bayesian seminonparametric approach to ARCH models is developed with the advantage that small sample results are obtained even when the likelihood function is subject to nonlinear inequality constraints (as in the ARCH models used in this paper). The seminonparametric nature of the approach allows for the relaxation of the assumption of normal errors. An application and a small Monte Carlo study indicate that the methods the author advocates are both feasible and necessary. Copyright 1994 by MIT Press.
Date: 1994
References: Add references at CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://links.jstor.org/sici?sici=0034-6535%2819940 ... 0.CO%3B2-S&origin=bc full text (application/pdf)
Access to full text is restricted to JSTOR subscribers. See http://www.jstor.org for details.
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:tpr:restat:v:76:y:1994:i:1:p:176-81
Ordering information: This journal article can be ordered from
https://mitpressjour ... rnal/?issn=0034-6535
Access Statistics for this article
The Review of Economics and Statistics is currently edited by Pierre Azoulay, Olivier Coibion, Will Dobbie, Raymond Fisman, Benjamin R. Handel, Brian A. Jacob, Kareen Rozen, Xiaoxia Shi, Tavneet Suri and Yi Xu
More articles in The Review of Economics and Statistics from MIT Press
Bibliographic data for series maintained by The MIT Press ().