Forecasting and conditional projection using realistic prior distribution
This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. We apply the procedure to 10 macroeconomic variables and show that it produces more accurate out-of-sample forecasts than univariate equations do. Although cross-variable responses are damped by the prior, our estimates capture considerable interaction among the variables. ; 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 analyze policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12. ; While no automatic casual interpretations arise from models like ours, such models provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables. That information may help evaluate casual hypotheses without containing any such hypotheses.
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- John Geweke, 1978.
"The Temporal and Sectoral Aggregation of Seasonally Adjusted Time Series,"
in: Seasonal Analysis of Economic Time Series, pages 411-432
National Bureau of Economic Research, Inc.
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The Review of Economics and Statistics,
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- Leamer, Edward E, 1972. "A Class of Informative Priors and Distributed Lag Analysis," Econometrica, Econometric Society, vol. 40(6), pages 1059-81, November.
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