Consensus and Uncertainty in Economic Prediction
The usual practice in economic forecasting is to report point predictions without specifying the attached probabilities. Periodic surveys of such forecasts produce group averages, which are taken to indicate the "consensus" of experts. Measures of the dispersion of individual forecasts around these averages are interpreted as indicating "uncertainty." However, consensus is best defined as the degree of agreement among the corresponding point predictions reported by different forecasters, while uncertainty is properly understood as referring to the diffuseness of the distributions of probabilities that individual forecasters attach to the different possible values of an economic variable. The NBER-ASA quarterly economic outlook surveys provide unique informationon probabilistic forecast distributions reported by a large number of individuals for changes in GNP and the implicit price deflator in 1969-81. These data permit comparisons of related point and probability forecasts from the same sources.The matched mean point forecasts and mean probability forecasts are found to agree closely. Standard deviations of point forecasts are generally smaller than the mean standard deviations of the predictive probability distributions for the same targets. Thus the former tend to understate uncertainty as measured by the latter. This is so particularly for short horizons.
|Date of creation:||Jul 1983|
|Publication status:||published as Zarnowitz, Victor and Louis A. Lambros. "Consensus and Uncertainty in Economic Prediction," Journal of Political Economy, Vol. 95, No. 3, pp. 591-621, June 1987.|
|Contact details of provider:|| Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.|
Web page: http://www.nber.org
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- Zarnowitz, Victor, 1982. "On Functions, Quality, and Timeliness of Economic Information," The Journal of Business, University of Chicago Press, vol. 55(1), pages 87-119, January.
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NBER Working Papers
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- Peter A. Morris, 1977. "Combining Expert Judgments: A Bayesian Approach," Management Science, INFORMS, vol. 23(7), pages 679-693, March.
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