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Conditional Forecasts In Dynamic Multivariate Models

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  • Daniel F. Waggoner
  • Tao Zha

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

Suggested Citation

  • Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    • Handle: RePEc:tpr:restat:v:81:y:1999:i:4:p:639-651
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