NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH

Forecasting and Conditional Projection Using Realistic Prior Distributions

Thomas Doan, Robert B. Litterman, Christopher A. Sims

NBER Working Paper No. 1202
Issued in September 1983
NBER Program(s):   EFG

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.

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Document Object Identifier (DOI): 10.3386/w1202

Published: Doan, Thomas, Robert B. Litterman and Christopher A. Sims. "Forecasting and Conditional Projection Using Realistic Prior Distributions," Econometric Reviews, Vol. 3, No. 1 Jan. 1984, pp. 1-100.

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