Forecasting and Conditional Projection Using Realistic Prior Distributions

81 Pages Posted: 3 May 2004 Last revised: 16 Oct 2022

See all articles by Thomas Doan

Thomas Doan

Independent

Robert Litterman

Kepos Capital

Christopher A. Sims

Princeton University - Department of Economics; National Bureau of Economic Research (NBER)

Date Written: September 1983

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.

Suggested Citation

Doan, Thomas and Litterman, Robert and Sims, Christopher A., Forecasting and Conditional Projection Using Realistic Prior Distributions (September 1983). NBER Working Paper No. w1202, Available at SSRN: https://ssrn.com/abstract=305579

Thomas Doan

Independent

Robert Litterman

Kepos Capital

620 Eighth Avenue
New York, NY 10018
United States

Christopher A. Sims (Contact Author)

Princeton University - Department of Economics ( email )

Princeton, NJ 08544-1021
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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