Forecasting and conditional projection using realistic prior distributions

Thomas Doan, Robert Litterman and Christopher Sims ()

Econometric Reviews, 1984, vol. 3, issue 1, pages 1-100

Abstract: This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied t o 10 macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variable 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 Although 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, information that may help in evaluating causal hypotheses without containing any such hypotheses.

Keywords: Rayesian Analysis; Conditional Projections; Forecasting; Macroeconomic Modeling; Vector Autoregressions (search for similar items in EconPapers)
Date: 1984
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Related works:
Working Paper: Forecasting and conditional projection using realistic prior distribution (1986)
Working Paper: Forecasting and Conditional Projection Using Realistic Prior Distributions (1983)
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