Numerical Methods for Estimation and Inference in Bayesian VAR-Models
K Rao Kadiyala and
Sune Karlsson ()
Journal of Applied Econometrics, 1997, vol. 12, issue 2, 99-132
In Bayesian analysis of vector autoregressive models, and especially in forecasting applications, the Minnesota prior of Litterman is frequently used. In many cases other prior distributions provided better forecasts and are preferable from a theoretical standpoint. Several of these priors require numerical methods in order to evaluate the posterior distribution. Different ways of implementing Monte Carlo integration are considered. It is found that Gibbs sampling performs as well as, or better, then importance sampling and that the Gibbs sampling algorithms are less adversely affected by model size. We also report on the forecasting performance of the different prior distributions
References: Add references at CitEc
Citations View citations in EconPapers (284) Track citations by RSS feed
Downloads: (external link)
http://qed.econ.queensu.ca:80/jae/1997-v12.2/ Supporting data files and programs (text/html)
Working Paper: Numerical Aspects of Bayesian VAR-modeling (1994)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:jae:japmet:v:12:y:1997:i:2:p:99-132
Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252
Access Statistics for this article
Journal of Applied Econometrics is currently edited by M. Hashem Pesaran
More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Series data maintained by Wiley-Blackwell Digital Licensing ().