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Numerical Methods for Estimation and Inference in Bayesian VAR-models
Krannert Graduate School of Management, Purdue University, W. Lafayette IN, USA.
Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden. (Stat@oru)ORCID iD: 0000-0003-0203-4688
1997 (English)In: Journal of applied econometrics (Chichester, England), ISSN 0883-7252, E-ISSN 1099-1255, Vol. 12, no 2, p. 99-132Article in journal (Refereed) Published
Abstract [en]

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 provide 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.

Place, publisher, year, edition, pages
John Wiley & Sons, 1997. Vol. 12, no 2, p. 99-132
National Category
Probability Theory and Statistics Economics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-61129DOI: 10.1002/(SICI)1099-1255(199703)12:2<99::AID-JAE429>3.0.CO;2-AISI: A1997WZ30600001Scopus ID: 2-s2.0-0039646598OAI: oai:DiVA.org:oru-61129DiVA, id: diva2:1144107
Available from: 2017-09-25 Created: 2017-09-25 Last updated: 2017-09-26Bibliographically approved

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Karlsson, Sune

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