Numerical Aspects of Bayesian VAR-modeling

K. Rao Kadiyala and Sune Karlsson ()
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K. Rao Kadiyala: Krannert Graduate School of Management, Purdue University

No 12, Working Paper Series in Economics and Finance from Stockholm School of Economics

Abstract: In Bayesian analysis of VAR-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. This paper considers the numerical procedures needed to implement these prior distributions. In addition we also report on the forecasting performance of the different prior distributions considered in the paper.

Keywords: Monte Carlo integration; importance sampling; Gibbs sampling; antithetic variates; forecasting (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp and nep-ets
Date: 1994-03
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Published in Journal of Applied Econometrics, 1997, pages 99-132

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http://swopec.hhs.se/hastef/papers/hastef0012.data.zip Data sets and Fortran code (application/zip)
http://swopec.hhs.se/hastef/papers/hastef0012.readme.txt Read me file (text/plain)

Related works:
Journal Article: Numerical Methods for Estimation and Inference in Bayesian VAR-Models (1997)
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Persistent link: http://EconPapers.repec.org/RePEc:hhs:hastef:0012

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