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Federal Reserve Bank of Atlanta
FRB Atlanta Working Paper
Conditional forecasts in dynamic multivariate models
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 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.


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Daniel F. Waggoner & Tao Zha, Conditional forecasts in dynamic multivariate models, Federal Reserve Bank of Atlanta, FRB Atlanta Working Paper 98-22, 1998.
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Keywords: Econometric models ; Forecasting ; Time-series analysis
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