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Copycats and Common Swings: The Impact of the Use of Forecasts in Information Sets

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  • Giampiero M. Gallo

    (International Monetary Fund)

  • Clive W.J. Granger

    (International Monetary Fund)

  • Yongil Jeon

    (International Monetary Fund)

Abstract

This paper presents evidence, using data from Consensus Forecasts, that there is an "attraction" to conform to the mean forecasts; in other words, views expressed by other forecasters in the previous period influence individuals' current forecast. The paper then discusses--and provides further evidence on--two important implications of this finding. The first is that the forecasting performance of these groups may be severely affected by the detected imitation behavior and lead to convergence to a value that is not the "right" target. Second, since the forecasts are not independent, the common practice of using the standard deviation from the forecasts' distribution, as if they were standard errors of the estimated mean, is not warranted. Copyright 2002, International Monetary Fund

Suggested Citation

  • Giampiero M. Gallo & Clive W.J. Granger & Yongil Jeon, 2002. "Copycats and Common Swings: The Impact of the Use of Forecasts in Information Sets," IMF Staff Papers, Palgrave Macmillan, vol. 49(1), pages 1-2.
  • Handle: RePEc:pal:imfstp:v:49:y:2002:i:1:p:2
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    References listed on IDEAS

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    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. Lamont, Owen A., 2002. "Macroeconomic forecasts and microeconomic forecasters," Journal of Economic Behavior & Organization, Elsevier, vol. 48(3), pages 265-280, July.
    3. Davies, Anthony & Lahiri, Kajal, 1995. "A new framework for analyzing survey forecasts using three-dimensional panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 205-227, July.
    4. David Laster & Paul Bennett & In Sun Geoum, 1999. "Rational Bias in Macroeconomic Forecasts," The Quarterly Journal of Economics, Oxford University Press, vol. 114(1), pages 293-318.
    5. Mills, Terence C. & Pepper, Gordon T., 1999. "Assessing the forecasters: an analysis of the forecasting records of the Treasury, the London Business School and the National Institute," International Journal of Forecasting, Elsevier, vol. 15(3), pages 247-257, July.
    6. 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.
    7. Victor Zarnowitz & Louis A. Lambros, 1983. "Consensus and Uncertainty in Economic Prediction," NBER Working Papers 1171, National Bureau of Economic Research, Inc.
    8. 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.
    9. John R. Graham, 1999. "Herding among Investment Newsletters: Theory and Evidence," Journal of Finance, American Finance Association, vol. 54(1), pages 237-268, February.
    10. 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.
    11. 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.
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    Cited by:

    1. Chang, Chia-Lin & de Bruijn, Bert & Franses, Philip Hans & McAleer, Michael, 2013. "Analyzing fixed-event forecast revisions," International Journal of Forecasting, Elsevier, vol. 29(4), pages 622-627.
    2. Stefan Günnel & Karl-Heinz Tödter, 2009. "Does Benford’s Law hold in economic research and forecasting?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 36(3), pages 273-292, August.
    3. Menz, Jan-Oliver & Poppitz, Philipp, 2013. "Households' disagreement on inflation expectations and socioeconomic media exposure in Germany," Discussion Papers 27/2013, Deutsche Bundesbank.
    4. Ager, P. & Kappler, M. & Osterloh, S., 2009. "The accuracy and efficiency of the Consensus Forecasts: A further application and extension of the pooled approach," International Journal of Forecasting, Elsevier, vol. 25(1), pages 167-181.
    5. Jonas Dovern & Ulrich Fritsche, 2008. "Estimating Fundamental Cross-Section Dispersion from Fixed Event Forecasts," Discussion Papers of DIW Berlin 787, DIW Berlin, German Institute for Economic Research.
    6. Dovern, Jonas, 2013. "When are GDP forecasts updated? Evidence from a large international panel," Economics Letters, Elsevier, vol. 120(3), pages 521-524.
    7. Jordi Pons-Novell, 2004. "Behavioural biases among interest rate forecasters?," Applied Economics Letters, Taylor & Francis Journals, vol. 11(5), pages 319-321.
    8. Michael P Clements, 2014. "Assessing the Evidence of Macro- Forecaster Herding: Forecasts of Inflation and Output Growth," ICMA Centre Discussion Papers in Finance icma-dp2014-12, Henley Business School, Reading University.
    9. Michael Groemling, 2005. "Konjunkturprognosen – Verfahren, Erfolgskontrolle und Prognosefehler," Departmental Discussion Papers 123, University of Goettingen, Department of Economics.
    10. repec:eee:finana:v:57:y:2018:i:c:p:90-105 is not listed on IDEAS
    11. Bernhardt, Dan & Campello, Murillo & Kutsoati, Edward, 2006. "Who herds?," Journal of Financial Economics, Elsevier, vol. 80(3), pages 657-675, June.
    12. Kaminska, Iryna & Roberts-Sklar, Matt, 2018. "Volatility in equity markets and monetary policy rate uncertainty," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 68-83.
    13. Kajal Lahiri & Gultekin Isiklar & Prakash Loungani, 2006. "How quickly do forecasters incorporate news? Evidence from cross-country surveys," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 703-725.
    14. Dovern, Jonas & Weisser, Johannes, 2011. "Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7," International Journal of Forecasting, Elsevier, vol. 27(2), pages 452-465.
    15. Isiklar, Gultekin & Lahiri, Kajal, 2007. "How far ahead can we forecast? Evidence from cross-country surveys," International Journal of Forecasting, Elsevier, vol. 23(2), pages 167-187.
    16. Dovern, Jonas & Fritsche, Ulrich & Loungani, Prakash & Tamirisa, Natalia, 2015. "Information rigidities: Comparing average and individual forecasts for a large international panel," International Journal of Forecasting, Elsevier, vol. 31(1), pages 144-154.
    17. Bruno Deschamps & Christos Ioannidis, 2014. "The Efficiency of Multivariate Macroeconomic Forecasts," Manchester School, University of Manchester, vol. 82(5), pages 509-523, September.
    18. Cameron Shelton, 2012. "The information content of elections and varieties of the partisan political business cycle," Public Choice, Springer, vol. 150(1), pages 209-240, January.
    19. Bizer, Kilian & Meub, Lukas & Proeger, Till & Spiwoks, Markus, 2014. "Strategic coordination in forecasting: An experimental study," Center for European, Governance and Economic Development Research Discussion Papers 195, University of Goettingen, Department of Economics.
    20. Rülke, Jan-Christoph & Silgoner, Maria & Wörz, Julia, 2016. "Herding behavior of business cycle forecasters," International Journal of Forecasting, Elsevier, vol. 32(1), pages 23-33.
    21. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.

    More about this item

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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