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Forecasting cointegrated series with BVAR models

✍ Scribed by Gianni Amisano; Massimiliano Serati

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 169 KB
Volume: 18
Category: Article
ISSN: 0277-6693
DOI: 10.1002/(sici)1099-131x(199912)18:7<463::aid-for732>3.0.co;2-d

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✦ Synopsis

In this paper we examine how BVARs can be used for forecasting cointegrated variables. We propose an approach based on a Bayesian ECM model in which, contrary to the previous literature, the factor loadings are given informative priors. This procedure, applied to Italian macroeconomic series, produces more satisfactory forecasts than dierent prior speci®cations or parameterizations. Providing an informative prior on the factor loadings is a crucial point: a ¯at prior on the ECM terms combined with an informative prior on the lagged endogenous variables coecients gives too much importance to the long-run properties with respect to the short-run dynamics.

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