✦ LIBER ✦

NUMERICAL METHODS FOR ESTIMATION AND INFERENCE IN BAYESIAN VAR-MODELS

✍ Scribed by K. RAO KADIYALA; SUNE KARLSSON

Publisher: John Wiley and Sons
Year: 1997
Tongue: English
Weight: 685 KB
Volume: 12
Category: Article
ISSN: 0883-7252
DOI: 10.1002/(sici)1099-1255(199703)12:2<99::aid-jae429>3.0.co;2-a

No coin nor oath required. For personal study only.

✦ Synopsis

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. Dierent 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 aected by model size. We also report on the forecasting performance of the dierent prior distributions. # 1997

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