Forecasting and Conditional Projection Using Realistic Prior Distributions

Tom Doan (), Robert Litterman and Christopher Sims ()

No 1202, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to ten macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variables responses are damped by the prior, considerable interaction among the variables is shown to be captured by the estimates.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 to analyze policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12.While no automatic causal interpretations arise from models like ours, they provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables, which may help inevaluating causal hypotheses, without containing any such hypotheses themselves.

Date: 1983-09
Note: EFG
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Related works:
Working Paper: Forecasting and conditional projection using realistic prior distribution (1986)
Journal Article: Forecasting and conditional projection using realistic prior distributions (1984)
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