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The impact of the use of forecasts in information sets

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  • Gallo, Giampiero M.
  • Granger, Clive William John
  • Jeon, Yongil

Abstract

We analyze the properties of multiperiod forecasts which are formulated by a number of companies for a fixed horizon ahead which moves each month one period closer and are collected and diffused each month by some polling agency. Some descriptive evidence and a formal model suggest that knowing the viewsexpressed by other forecasters the previous period is influencing individual current forecasts in the form of an attraction to conform to the mean forecast. There are two implications: one is that the forecasts polled in a multiperiod framework cannot be seen as independent from one another and hence the practice of using standard deviations from the forecasts' distribution as if they were standard errors of the estimated mean is not warranted. The second is that the forecasting performance of these groups may be severely affected by the detected imitation behavior and lead to convergence to a value which is not the right target (either the first available figure or some final values available at a later time). --

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Bibliographic Info

Paper provided by Deutsche Bank Research in its series Research Notes with number 99-7.

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Date of creation: 1999
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Handle: RePEc:zbw:dbrrns:997

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Related research

Keywords: multistep forecast; consensus forecast; preliminary data;

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References

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  1. Swanson, N.R., 1996. "Forecasting Using First Available Versus Fully Revised Economic Time Series data," Papers 4-96-7, Pennsylvania State - Department of Economics.
  2. Batchelor, Roy & Dua, Pami, 1998. "Improving macro-economic forecasts: The role of consumer confidence," International Journal of Forecasting, Elsevier, vol. 14(1), pages 71-81, March.
  3. John R. Graham, 1999. "Herding among Investment Newsletters: Theory and Evidence," Journal of Finance, American Finance Association, vol. 54(1), pages 237-268, 02.
  4. Granger, Clive W J, 1996. "Can We Improve the Perceived Quality of Economic Forecasts?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 455-73, Sept.-Oct.
  5. David E. Runkle, 1998. "Revisionist history: how data revisions distort economic policy research," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall, pages 3-12.
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Cited by:
  1. Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, vol. 21(2), pages 323-343, March.
  2. Alfredo Pistelli M., 2012. "Análisis de Sesgos y Eficiencia en Proyecciones de Consensus Forecasts," Notas de Investigación Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 15(1), pages 98-104, April.
  3. Krekó, Judit & Vonnák, Balázs, 2003. "Makroelemzők inflációs várakozásai Magyarországon
    [The inflationary expectations of macro analysts in Hungary]
    ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(4), pages 315-334.

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