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Comparing for Different Time Series Methods the Value of Technical Expertise Individualized Analysis, and Judgmental Adjustment

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Author Info

  • Robert Carbone

    (Université Laval, Quebec, Canada)

  • Allan Andersen

    (University of Sydney, Australia)

  • Yvan Corriveau

    (Université Laval, Quebec, Canada)

  • Paul Piat Corson

    (Université Laval, Quebec, Canada)

Registered author(s):

    Abstract

    Technical expertise, human judgment, and the time spent by an analyst are often believed to be key factors in determining the accuracy of forecasts obtained with the use of a time series forecasting method. A control experiment was designed to empirically test these beliefs. It involved the participation of experts and persons with limited training. Forecasts were generated for 25 time series with the use of the Box-Jenkins, Holt-Winters and Carbone-Longini filtering methods. Results of the nonparametric tests used to compare the forecasts confirmed that technical expertise, judgmental adjustment, and individualized analyses were of little value in improving forecast accuracy as compared to black box approaches. In addition, simpler methods were found to provide significantly more accurate forecasts than the Box-Jenkins method when applied by persons with limited training.

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    File URL: http://dx.doi.org/10.1287/mnsc.29.5.559
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 29 (1983)
    Issue (Month): 5 (May)
    Pages: 559-566

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    Handle: RePEc:inm:ormnsc:v:29:y:1983:i:5:p:559-566

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    Related research

    Keywords: forecasting/time series;

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    Cited by:
    1. Welch, Eric & Bretschneider, Stuart & Rohrbaugh, John, 1998. "Accuracy of judgmental extrapolation of time series data: Characteristics, causes, and remediation strategies for forecasting," International Journal of Forecasting, Elsevier, vol. 14(1), pages 95-110, March.
    2. Deschamps, Elaine, 2004. "The impact of institutional change on forecast accuracy: A case study of budget forecasting in Washington State," International Journal of Forecasting, Elsevier, vol. 20(4), pages 647-657.
    3. Vokurka, Robert J. & Flores, Benito E. & Pearce, Stephen L., 1996. "Automatic feature identification and graphical support in rule-based forecasting: a comparison," International Journal of Forecasting, Elsevier, vol. 12(4), pages 495-512, December.
    4. Eroglu, Cuneyt & Croxton, Keely L., 2010. "Biases in judgmental adjustments of statistical forecasts: The role of individual differences," International Journal of Forecasting, Elsevier, vol. 26(1), pages 116-133, January.

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