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Simple robust averages of forecasts: Some empirical results

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  • Jose, Victor Richmond R.
  • Winkler, Robert L.
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    Abstract

    An extensive body of literature has shown that combining forecasts can improve forecast accuracy, and that a simple average of the forecasts (the mean) often does better than more complex combining schemes. The fact that the mean is sensitive to extreme values suggests that deleting such values or reducing their extremity might be worthwhile. We study the performance of two simple robust methods, trimmed and Winsorized means, which are easy to use and understand. For the data sets we consider, they provide forecasts which are slightly more accurate than the mean, and reduce the risk of high errors. Our results suggest that moderate trimming of 10-30% or Winsorizing of 15-45% of the forecasts can provide improved combined forecasts, with more trimming or Winsorizing being indicated when there is more variability among the individual forecasts. There are some differences in the performance of the trimmed and Winsorized means, but overall such differences are not large.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 24 (2008)
    Issue (Month): 1 ()
    Pages: 163-169

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    Handle: RePEc:eee:intfor:v:24:y:2008:i:1:p:163-169

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    Web page: http://www.elsevier.com/locate/ijforecast

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    References

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    1. Clemon, Robert T & Winkler, Robert L, 1986. "Combining Economic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 4(1), pages 39-46, January.
    2. Yaniv, Ilan, 1997. "Weighting and Trimming: Heuristics for Aggregating Judgments under Uncertainty," Organizational Behavior and Human Decision Processes, Elsevier, Elsevier, vol. 69(3), pages 237-249, March.
    3. Dean Croushore, 1993. "Introducing: the survey of professional forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 3-15.
    4. Graham, John R, 1996. "Is a Group of Economists Better than One? Than None?," The Journal of Business, University of Chicago Press, vol. 69(2), pages 193-232, April.
    5. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    6. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, Elsevier, vol. 5(4), pages 559-583.
    7. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, Elsevier, vol. 9(4), pages 527-529, December.
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    Cited by:
    1. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, Elsevier, vol. 30(1), pages 43-54.
    2. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
    3. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(2), pages 238-251, April.
    4. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, Elsevier, vol. 30(2), pages 291-302.
    5. Fabian Krueger & Frieder Mokinski & Winfried Pohlmeier, 2011. "Combining Survey Forecasts and Time Series Models: The Case of the Euribor," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(1), pages 63-81, February.
    6. Fabian Krüger & Ingmar Nolte, 2011. "Disagreement, Uncertainty and the True Predictive Density," Working Paper Series of the Department of Economics, University of Konstanz 2011-43, Department of Economics, University of Konstanz.
    7. Fildes, Robert & Kourentzes, Nikolaos, 2011. "Validation and forecasting accuracy in models of climate change," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(4), pages 968-995, October.
    8. Grant, Andrew & Johnstone, David, 2010. "Finding profitable forecast combinations using probability scoring rules," International Journal of Forecasting, Elsevier, Elsevier, vol. 26(3), pages 498-510, July.
    9. Antonis Michis, 2012. "Monitoring Forecasting Combinations with Semiparametric Regression Models," Working Papers, Central Bank of Cyprus 2012-02, Central Bank of Cyprus.

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