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The impact of the use of forecasts in information sets

Author

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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).

Suggested Citation

  • Gallo, Giampiero M. & Granger, Clive William John & Jeon, Yongil, 1999. "The impact of the use of forecasts in information sets," Research Notes 99-7, Deutsche Bank Research.
  • Handle: RePEc:zbw:dbrrns:997
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    References listed on IDEAS

    as
    1. 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.
    2. Swanson Norman, 1996. "Forecasting Using First-Available Versus Fully Revised Economic Time-Series Data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(1), pages 1-20, April.
    3. 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 455-473, Sept.-Oct.
    4. John R. Graham, 1999. "Herding among Investment Newsletters: Theory and Evidence," Journal of Finance, American Finance Association, vol. 54(1), pages 237-268, February.
    5. 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.
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    Citations

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    Cited by:

    1. Loungani, Prakash, 2001. "How accurate are private sector forecasts? Cross-country evidence from consensus forecasts of output growth," International Journal of Forecasting, Elsevier, vol. 17(3), pages 419-432.
    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. Blanca Moreno & Ana Jesus Lopez, 2007. "Combining economic forecasts through information measures," Applied Economics Letters, Taylor & Francis Journals, vol. 14(12), pages 899-903.
    4. Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, vol. 21(2), pages 323-343, March.
    5. 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.

    More about this item

    Keywords

    multistep forecast; consensus forecast; preliminary data;

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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