Forecasting and conditional projection using realistic prior distribution

Thomas Doan, Robert Litterman and Christopher Sims ()

No 93, Staff Report from Federal Reserve Bank of Minneapolis

Abstract: This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. We apply the procedure to 10 macroeconomic variables and show that it produces more accurate out-of-sample forecasts than univariate equations do. Although cross-variable responses are damped by the prior, our estimates capture considerable interaction among the variables. ; We provide unconditional forecasts as of 1982:12 and 1983:3. We also describe how a model such as this can be used to make conditional projections and analyze policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12. ; While no automatic casual interpretations arise from models like ours, such models provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables. That information may help evaluate casual hypotheses without containing any such hypotheses.

Date: 1986
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Related works:
Working Paper: Forecasting and Conditional Projection Using Realistic Prior Distributions (1983)
Journal Article: Forecasting and conditional projection using realistic prior distributions (1984)
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