Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/100769
Authors: 
Waggoner, Daniel F.
Zha, Tao
Year of Publication: 
1998
Series/Report no.: 
Working Paper, Federal Reserve Bank of Atlanta 98-22
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.
Subjects: 
Econometric models
Forecasting
Time-series analysis
Document Type: 
Working Paper

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