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Averaging Model Forecasts and Expert Forecasts: Why Does It Work?

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  • Philip Hans Franses

    (Econometric Institute, Erasmus University Rotterdam, 3000 DR Rotterdam, The Netherlands)

Abstract

This paper addresses a situation in which a manager has forecasts of country-specific stock-keeping unit (SKU)-level sales data from both a statistical model and from a range of experts. Empirical evidence suggests that averaging model forecasts and expert forecasts could give more accuracy than the individual forecasts do. At the same time, empirical evidence demonstrates that expert forecasts tend to be biased. So, why is it that this average could work? This paper assumes that expert forecasts can be decomposed into a judgmental bootstrapping equation, to be created by the manager, and a part based on unobserved intuition. When the bootstrapping equation dominates the intuition, it is this underlying model for the expert that can make the combined average forecast more accurate. An illustration for no less than 1,221 cases supports this conjecture.

Suggested Citation

  • Philip Hans Franses, 2011. "Averaging Model Forecasts and Expert Forecasts: Why Does It Work?," Interfaces, INFORMS, vol. 41(2), pages 177-181, April.
    • Handle: RePEc:inm:orinte:v:41:y:2011:i:2:p:177-181
    DOI: 10.1287/inte.1100.0554
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    References listed on IDEAS

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      • Boulaksil, Y. & Franses, Ph.H.B.F., 2008. "Experts' Stated Behavior," ERIM Report Series Research in Management ERS-2008-001-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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    Cited by:

    1. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    2. Antonis Michis, 2012. "Monitoring Forecasting Combinations with Semiparametric Regression Models," Working Papers 2012-2, Central Bank of Cyprus.
    3. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    4. Antonis Michis, 2012. "Monitoring Forecasting Combinations with Semiparametric Regression Models," Working Papers 2012-02, Central Bank of Cyprus.

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