On the Performance and Use of Government Revenue Forecasts
AbstractThis paper considers the performance of government forecasts--by the Congressional Budget Office (CBO) and the Office of Management and Budget (OMB)--from the standpoint of bias and efficiency. The results are mixed. On the one hand, their performance has not differed significantly from that of a private forecaster, Data Resources, Inc. (DRI). Further, even when the sample period is broken down into "pessimistic" and "optimistic" periods, forecast errors have such large standard errors that it is difficult to conclude that the forecasts exhibit any underlying bias. On the other hand, the government forecasts fail statistical tests of efficiency. In particular, forecast revisions exhibit significant serial correlation and strong seasonality. These results suggest that government revenue forecasts could, in principle, convey more information than they do at present. Also, the large standard errors of the forecasts stand at odds with the use of point estimates for policy purposes. Because the budget process ignores forecast uncertainty, the "best" forecasts for budget purposes need not be the most accurate point estimates; it might well be appropriate, for example, for forecasts to reflect a pessimistic bias. Thus, the requirements of forecast efficiency and those of a poorly conceived budget process are inconsistent.
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Bibliographic InfoPaper provided by Berkeley Olin Program in Law & Economics in its series Berkeley Olin Program in Law & Economics, Working Paper Series with number qt8h845262.
Date of creation: 20 Jul 1999
Date of revision:
Other versions of this item:
- Auerbach, Alan J., 1999. "On the Performance and Use of Government Revenue Forecasts," National Tax Journal, National Tax Association, vol. 52(n. 4), pages 765-82, December .
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- Bengt Holmstrom, 1981.
"Moral Hazard in Teams,"
471, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Campbell, Bryan & Ghysels, Eric, 1995. "Federal Budget Projections: A Nonparametric Assessment of Bias and Efficiency," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 17-31, February.
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