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Statistical correction of judgmental point forecasts and decisions

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  • Goodwin, P.
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    Abstract

    In many organizations point estimates labelled as 'forecasts' are produced by human judgment rather than statistical methods. However, when these estimates are subject to asymmetric loss they are, in fact, decisions because they involve the selection of a value with the objective of minimizing loss. While there are often considerable advantages in using judgment to arrive at these decisions the psychological demands of the task may mean that the resulting decisions are sub-optimal when compared with those resulting from a normative decision model. In these circumstances a combination of statistical methods and judgment may be superior. This paper suggests a procedure which involves the statistical correction of the original decision to obtain a forecast and the subsequent use of a mathematical model to identify the theoretically optimal decision in the light of this forecast. The application of the procedure to the monthly decisions of a manufacturing company suggests that it may offer the potential for achieving substantial improvements in many practical contexts.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 24 (1996)
    Issue (Month): 5 (October)
    Pages: 551-559

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    Handle: RePEc:eee:jomega:v:24:y:1996:i:5:p:551-559

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    Related research

    Keywords: bias decision making forecasting judgment;

    References

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    1. M. J. Lawrence & R. H. Edmundson & M. J. O'Connor, 1986. "The Accuracy of Combining Judgemental and Statistical Forecasts," Management Science, INFORMS, vol. 32(12), pages 1521-1532, December.
    2. Steven D. Wood & Bert M. Steece, 1978. "Forecasting the Product of Two Time Series with a Linear Asymmetric Error Cost Function," Management Science, INFORMS, vol. 24(6), pages 690-701, February.
    3. Dalrymple, Douglas J., 1987. "Sales forecasting practices: Results from a United States survey," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 379-391.
    4. Robert C. Blattberg & Stephen J. Hoch, 1990. "Database Models and Managerial Intuition: 50% Model + 50% Manager," Management Science, INFORMS, vol. 36(8), pages 887-899, August.
    5. Goodwin, P & Wright, G, 1994. "Heuristics, biases and improvement strategies in judgmental time series forecasting," Omega, Elsevier, vol. 22(6), pages 553-568, November.
    6. Goodwin, Paul & Wright, George, 1993. "Improving judgmental time series forecasting: A review of the guidance provided by research," International Journal of Forecasting, Elsevier, vol. 9(2), pages 147-161, August.
    7. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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    Citations

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    Cited by:
    1. ├ľnkal, Dilek & Bolger, Fergus, 2004. "Provider-user differences in perceived usefulness of forecasting formats," Omega, Elsevier, vol. 32(1), pages 31-39, February.
    2. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    3. JS Armstrong & Fred Collopy, 2004. "Integration of Statistical Methods and Judgment for Time Series," General Economics and Teaching 0412024, EconWPA.
    4. Goodwin, Paul, 2005. "Providing support for decisions based on time series information under conditions of asymmetric loss," European Journal of Operational Research, Elsevier, vol. 163(2), pages 388-402, June.
    5. Lee, Yun Shin, 2014. "A semi-parametric approach for estimating critical fractiles under autocorrelated demand," European Journal of Operational Research, Elsevier, vol. 234(1), pages 163-173.
    6. Goodwin, Paul, 2000. "Correct or combine? Mechanically integrating judgmental forecasts with statistical methods," International Journal of Forecasting, Elsevier, vol. 16(2), pages 261-275.

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