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Is there a Golden Rule?

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  • Fildes, Robert
  • Petropoulos, Fotios

Abstract

Armstrong, Green, and Graefe (this issue) propose the Golden Rule in forecasting: “be conservative”. According to the authors, the successful application of the Golden Rule comes through a checklist of 28 guidelines. Even if the authors of this commentary embrace the main ideas around the Golden Rule, which targets to address the “average” situation, they believe that this rule should not be applied automatically. There is no universal extrapolation method that can tackle every forecasting problem; nor are there simple rules that automatically apply without reference to the data. Similarly, it is demonstrated that for a specific causal regression model the recommended conservative rule leads to unnecessary inaccuracy. In this commentary the authors demonstrate, using the power of counter examples, two cases where the Golden Rule fails. Forecasting performance is context-dependent and, as such, forecasters (researchers and practitioners) should take into account the specific features of the situation faced.

Suggested Citation

  • Fildes, Robert & Petropoulos, Fotios, 2015. "Is there a Golden Rule?," Journal of Business Research, Elsevier, vol. 68(8), pages 1742-1745.
    • Handle: RePEc:eee:jbrese:v:68:y:2015:i:8:p:1742-1745
    DOI: 10.1016/j.jbusres.2015.01.059
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    References listed on IDEAS

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    Cited by:

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    2. López Menéndez, Ana Jesús & Pérez Suárez, Rigoberto, 2017. "Forecasting Performance and Information Measures. Revisiting the M-Competition /Evaluación de Predicciones y Medidas de Información. Reexamen de la M-Competición," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 35, pages 299-314, Mayo.
    3. Katsikopoulos, Konstantinos V. & Durbach, Ian N. & Stewart, Theodor J., 2018. "When should we use simple decision models? A synthesis of various research strands," Omega, Elsevier, vol. 81(C), pages 17-25.
    4. 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.
    5. Salim Lahmiri, 2020. "A predictive system integrating intrinsic mode functions, artificial neural networks, and genetic algorithms for forecasting S&P500 intra‐day data," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(2), pages 55-65, April.

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