Forecasting and conditional projection using realistic prior distributions (with discussion). (English) Zbl 0613.62142

This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to 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.


62P20 Applications of statistics to economics
62F15 Bayesian inference
62M20 Inference from stochastic processes and prediction
91B84 Economic time series analysis
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