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Golden rule of forecasting rearticulated: Forecast unto others as you would have them forecast unto you

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  • Green, Kesten C.
  • Armstrong, J. Scott
  • Graefe, Andreas

Abstract

The Golden Rule of Forecasting is a general rule that applies to all forecasting problems. The Rule was developed using logic and was tested against evidence from previously published comparison studies. The evidence suggests that a single violation of the Golden Rule is likely to increase forecast error by 44%. Some commentators argue that the Rule is not generally applicable, but do not challenge the logic or evidence provided. While further research might provide useful findings, available evidence justifies adopting the Rule now. People with no prior training in forecasting can obtain the substantial benefits of following the Golden Rule by using the Checklist to identify biased and unscientific forecasts at little cost.

Suggested Citation

  • Green, Kesten C. & Armstrong, J. Scott & Graefe, Andreas, 2015. "Golden rule of forecasting rearticulated: Forecast unto others as you would have them forecast unto you," Journal of Business Research, Elsevier, vol. 68(8), pages 1768-1771.
    • Handle: RePEc:eee:jbrese:v:68:y:2015:i:8:p:1768-1771
    DOI: 10.1016/j.jbusres.2015.03.036
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    References listed on IDEAS

    as
    1. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    2. Soyer, Emre & Hogarth, Robin M., 2015. "The golden rule of forecasting: Objections, refinements, and enhancements," Journal of Business Research, Elsevier, vol. 68(8), pages 1702-1704.
    3. Fildes, Robert & Petropoulos, Fotios, 2015. "Is there a Golden Rule?," Journal of Business Research, Elsevier, vol. 68(8), pages 1742-1745.
    4. Goodwin, Paul, 2015. "Is a more liberal approach to conservatism needed in forecasting?," Journal of Business Research, Elsevier, vol. 68(8), pages 1753-1754.
    5. Cass Sunstein & Richard Zeckhauser, 2011. "Overreaction to Fearsome Risks," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(3), pages 435-449, March.
    6. Soyer, Emre & Hogarth, Robin M., 2012. "The illusion of predictability: How regression statistics mislead experts," International Journal of Forecasting, Elsevier, vol. 28(3), pages 695-711.
    7. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    8. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    9. Gardner, Everette S., 2015. "Conservative forecasting with the damped trend," Journal of Business Research, Elsevier, vol. 68(8), pages 1739-1741.
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    Cited by:

    1. Thomson, Mary E. & Pollock, Andrew C. & Önkal, Dilek & Gönül, M. Sinan, 2019. "Combining forecasts: Performance and coherence," International Journal of Forecasting, Elsevier, vol. 35(2), pages 474-484.
    2. Cameron, Andrew & Nelson, Rohan, 2022. "Enabling Users to Evaluate the Accuracy of ABARES Agricultural Forecasts," Australasian Agribusiness Review, University of Melbourne, Department of Agriculture and Food Systems, vol. 30(7), November.

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