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Does Disagreement Amongst Forecasters have Predictive Value?

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  • Legerstee, R.
  • Franses, Ph.H.B.F.

Abstract

Forecasts from various experts are often used in macroeconomic forecasting models. Usually the focus is on the mean or median of the survey data. In the present study we adopt a different perspective on the survey data as we examine the predictive power of disagreement amongst forecasters. The premise is that this variable could signal upcoming structural or temporal changes in an economic process or in the predictive power of the survey forecasts. In our empirical work, we examine a variety of macroeconomic variables, and we use different measurements for the degree of disagreement, together with measures for location of the survey data and autoregressive components. Forecasts from simple linear models and forecasts from Markov regime-switching models with constant and with time-varying transition probabilities are constructed in real-time and compared on forecast accuracy. We find that disagreement has predictive power indeed and that this variable can be used to improve forecasts when used in Markov regime-switching models.

Suggested Citation

  • Legerstee, R. & Franses, Ph.H.B.F., 2010. "Does Disagreement Amongst Forecasters have Predictive Value?," Econometric Institute Research Papers EI 2010-53, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:20744
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    References listed on IDEAS

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    1. Victor Zarnowitz & Louis A. Lambros, 1983. "Consensus and Uncertainty in Economic Prediction," NBER Working Papers 1171, National Bureau of Economic Research, Inc.
    2. Kajal Lahiri & Xuguang Sheng, 2010. "Measuring forecast uncertainty by disagreement: The missing link," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 514-538.
    3. Graham Elliott & Allan Timmermann, 2005. "Optimal Forecast Combination Under Regime Switching ," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(4), pages 1081-1102, November.
    4. Lamont, Owen A., 2002. "Macroeconomic forecasts and microeconomic forecasters," Journal of Economic Behavior & Organization, Elsevier, vol. 48(3), pages 265-280, July.
    5. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    6. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    7. Carlos Capistrán & Allan Timmermann, 2009. "Disagreement and Biases in Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(2-3), pages 365-396, March.
    8. Zacharias Psaradakis & Nicola Spagnolo, 2006. "Joint Determination of the State Dimension and Autoregressive Order for Models with Markov Regime Switching," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 753-766, September.
    9. Timmermann, Allan, 2000. "Moments of Markov switching models," Journal of Econometrics, Elsevier, vol. 96(1), pages 75-111, May.
    10. 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.
    11. Smith, Aaron & Naik, Prasad A. & Tsai, Chih-Ling, 2006. "Markov-switching model selection using Kullback-Leibler divergence," Journal of Econometrics, Elsevier, vol. 134(2), pages 553-577, October.
    12. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    13. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    14. Aurelien Baillon & Laure Cabantous, 2009. "Combining imprecise or conflicting probability judgments: A choice-based study," ICBBR Working Papers 3, International Centre for Behavioural Business Research.
    15. 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.
    16. Mc Cracken, Michael W., 2000. "Robust out-of-sample inference," Journal of Econometrics, Elsevier, vol. 99(2), pages 195-223, December.
    17. Webby, Richard & O'Connor, Marcus, 1996. "Judgemental and statistical time series forecasting: a review of the literature," International Journal of Forecasting, Elsevier, vol. 12(1), pages 91-118, March.
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    Cited by:

    1. Nautz, Dieter & Pagenhardt, Laura & Strohsal, Till, 2017. "The (de-)anchoring of inflation expectations: New evidence from the euro area," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 103-115.

    More about this item

    Keywords

    Markov regime-switching models; disagreement; expert forecasts; model forecasts; survey forecasts; time series;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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