Conditional forecasts in dynamic multivariate models

Daniel Waggoner and Tao Zha

No 98-22, FRB Atlanta Working Paper from Federal Reserve Bank of Atlanta

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: Econometric models; Forecasting; time series analysis (search for similar items in EconPapers)
Date: 1998
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (56)

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Journal Article: Conditional Forecasts In Dynamic Multivariate Models (1999)
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