Conditional Forecasts In Dynamic Multivariate Models

Daniel Waggoner and Tao Zha

The Review of Economics and Statistics, 1999, vol. 81, issue 4, 639-651

Abstract: In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions. This paper develops Bayesian methods for computing the exact finite-sample distribution of conditional forecasts. 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 parameter uncertainty in finite samples. Empirical examples under both a flat prior and a reference prior are provided to show the use of these methods. © 2000 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

Date: 1999
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Working Paper: Conditional forecasts in dynamic multivariate models (1998)
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