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The Record and Improvability of Economic Forecasting

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  • Victor Zarnowitz
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    Abstract

    Have macroeconomic forecasts grown more or less accurate over time? This paper assembles, examines, and interprets evidence bearing on this question. Contrary to some critics, there are no indications that U.S. forecasts have grown systematically worse, that is, less accurate, more biased, or both. Neither do any definite trends in a positive direction emerge from comparisons of annual and quarterly multiperiod forecasts and time-series projections for the principal aggregative variables. The argument is developed and to some extent documented that major failures of forecasting are related to the incidence of slowdowns and contractions in general economic activity. Not only the forecasts of real GNP growth and unemployment but also those of nominal GNP growth and inflation often go seriously wrong when such setbacks occur. Forecasters tend to rely heavily on the persistence of trends in spending, output, and the price level. More attention to data and techniques that are sensitive to business cycle movements and turning points could help improve their record.

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    File URL: http://www.nber.org/papers/w2099.pdf
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    Bibliographic Info

    Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 2099.

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    Date of creation: Dec 1986
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    Publication status: published as Zarnowitz, Victor. "The Record and Improvability of Economic Forecasting." Economic Forecasts: A Worldwide Survey, Vol. 3, No. 12, (December 1986), pp. 22-30.
    Handle: RePEc:nbr:nberwo:2099

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    1. McNees, Stephen K, 1986. "Forecasting Accuracy of Alternative Techniques: A Comparison of U.S. Macroeconomic Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 4(1), pages 23, January.
    2. Granger, C W J, 1986. "Forecasting Accuracy of Alternative Techniques: A Comparison of U.S. Macroeconomic Forecasts: Comment," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 4(1), pages 16-17, January.
    3. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, American Economic Association, vol. 62(4), pages 540-52, September.
    4. Beatrice N. Vaccara & Victor Zarnowitz, 1978. "Forecasting with the Index of Leading Indicators," NBER Working Papers 0244, National Bureau of Economic Research, Inc.
    5. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, Elsevier, vol. 1(1), pages 19-46, January.
    6. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, Econometric Society, vol. 37(3), pages 424-38, July.
    7. Neftici, Salih N., 1982. "Optimal prediction of cyclical downturns," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 4(1), pages 225-241, November.
    8. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 4(1), pages 25-38, January.
    9. Lupoletti, William M & Webb, Roy H, 1986. "Defining and Improving the Accuracy of Macroeconomic Forecasts: Contributions from a VAR Model," The Journal of Business, University of Chicago Press, University of Chicago Press, vol. 59(2), pages 263-85, April.
    10. Kling, John L, 1987. "Predicting the Turning Points of Business and Economic Time Series," The Journal of Business, University of Chicago Press, University of Chicago Press, vol. 60(2), pages 201-38, April.
    11. McNees, Stephen K, 1986. "Forecasting Accuracy of Alternative Techniques: A Comparison of U.S. Macroeconomic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 4(1), pages 5-15, January.
    12. Wecker, William E, 1979. "Predicting the Turning Points of a Time Series," The Journal of Business, University of Chicago Press, University of Chicago Press, vol. 52(1), pages 35-50, January.
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    Cited by:
    1. Natalia T. Tamirisa & Prakash Loungani & Herman O. Stekler, 2011. "Information Rigidity in Growth Forecasts," IMF Working Papers 11/125, International Monetary Fund.

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