Forecasting and conditional projection using realistic prior distributions |
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Authors: | Thomas Doan Robert Litterman Christopher Sims |
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Institution: |
a Northwestern University,
b Federal Reserve Rank of Minneapolis,
c University of Minnesota, |
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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. |
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Keywords: | Rayesian Analysis Conditional Projections Forecasting Macroeconomic Modeling Vector Autoregressions |
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