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Diversity, uncertainty, and accuracy of inflation forecasts

  • Stephen K. McNees
  • Lauren K. Fine
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    Uncertainty is a key concept in both economic theory and economic practice. Yet, economic forecasts are usually stated as single numbers, or "point estimates," that convey no information about the full array of possible outcomes. The dispersion of individual forecasters' point estimates is often used as an approximation of forecast uncertainty, even though it is neither logically nor empirically related. In fact, the diversity of point estimates is a poor guide to the accuracy of a point estimate forecast. ; This article examines explicit estimates of forecast uncertainty, taken from the Survey of Professional Forecasters. It concludes that most individuals' estimates of ~nflation and real GNP uncertainty are well calibrated at both the 50 and 90 percent, though not at the 100 percent, confidence intervals. In contrast, the mean probability distribution of all respondents is well calibrated at all three intervals. Despite their overall reliability, the uncertainty estimates are not correlated with the accuracy of point estimate forecasts. This lack of correlation should not be construed as evidence that uncertainty cannot be reliably anticipated, however.

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    File URL: http://www.bostonfed.org/economic/neer/neer1994/neer494b.pdf
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    Article provided by Federal Reserve Bank of Boston in its journal New England Economic Review.

    Volume (Year): (1994)
    Issue (Month): Jul ()
    Pages: 33-44

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    Handle: RePEc:fip:fedbne:y:1994:i:jul:p:33-44
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    1. Stephen K. McNees, 1992. "How large are economic forecast errors?," New England Economic Review, Federal Reserve Bank of Boston, issue Jul, pages 25-42.
    2. Zarnowitz, Victor, 1985. "Rational Expectations and Macroeconomic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(4), pages 293-311, October.
    3. Victor Zarnowitz & Phillip Braun, 1993. "Twenty-two Years of the NBER-ASA Quarterly Economic Outlook Surveys: Aspects and Comparisons of Forecasting Performance," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 11-94 National Bureau of Economic Research, Inc.
    4. Zarnowitz, Victor & Lambros, Louis A, 1987. "Consensus and Uncertainty in Economic Prediction," Journal of Political Economy, University of Chicago Press, vol. 95(3), pages 591-621, June.
    5. Dean Croushore, 1993. "Introducing: the survey of professional forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 3-15.
    6. Lahiri, Kajal & Zaporowski, Mark, 1987. "More Flexible Use of Survey Data on Expectations in Macroeconomic Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(1), pages 68-76, January.
    7. Engle, Robert F, 1983. "Estimates of the Variance of U.S. Inflation Based upon the ARCH Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 15(3), pages 286-301, August.
    8. Lahiri, Kajal & Teigland, Christie & Zaporowski, Mark, 1988. "Interest Rates and the Subjective Probability Distribution of Inflation Forecasts," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 20(2), pages 233-48, May.
    9. Victor Zarnowitz & Louis A. Lambros, 1983. "Consensus and Uncertainty in Economic Prediction," NBER Working Papers 1171, National Bureau of Economic Research, Inc.
    10. Lahiri, Kajal & Teigland, Christie, 1987. "On the normality of probability distributions of inflation and GNP forecasts," International Journal of Forecasting, Elsevier, vol. 3(2), pages 269-279.
    11. R. W. Hafer & Scott E. Hein, 1984. "On the accuracy of time series, interest rate and survey forecasts of inflation," Working Papers 1984-022, Federal Reserve Bank of St. Louis.
    12. Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-44, April.
    13. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-35, April.
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