Conditional Forecasts in Dynamic Multivariate Models
43 Pages Posted: 25 Jan 2015
Date Written: December 1998
Abstract
In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions or error bands. This paper develops Bayesian methods for computing such distributions or bands. It broadens the class of conditional forecasts to which the methods can be applied. The methods work for both structural and reduced-form VAR models and, in contrast to common practices, account for the parameter uncertainty in small samples. Empirical examples under the flat prior and under the reference prior of Sims and Zha (1998) are provided to show the use of these methods.
Keywords: conditional forecasts, hard and soft conditions, Bayesian methods, probability distribution, error bands, likelihood
JEL Classification: C32, E17, C53
Suggested Citation: Suggested Citation
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