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Forecasting and conditional projection using realistic prior distributions

Working Paper 243 | Published August 1, 1983

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Authors

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Thomas Doan

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Robert B. Litterman

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Christopher A. Sims

Forecasting and conditional projection using realistic prior distributions

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 1963:3* We also describe how a model such as this can be used to make conditional projections and to analyse 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 in evaluating causal hypotheses, without containing any such hypotheses themselves.


Published in _Econometric Reviews_ (vol. 3, iss. 1, 1984, pp. 1-100), https://doi.org/10.1080/07474938408800053.