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Commentary by J. Scott Armstrong on Fildes et al.: Generalizing about univariate forecasting methods: further empirical evidence


  • J. S. Armstrong

    (The Wharton School)


Fildes, Hibon, Makridakis and Meade (1998), which will be referred to as FHMM, extends two important published papers. The idea of taking findings from each study and testing them against the data used in the other study is a good one. Such replications and extensions are important in the effort to develop useful generalizations and publication of this paper reflects the commitment of International Journal of Forecasting to replication research. In addition the study examines procedures for estimating smoothing parameters, and it evaluates the need for using multiple starting points when evaluating forecasting methods. On the negative side, FHMM does not fully describe the conditions under which one might expect a given extrapolation method to provide more accurate forecasts than competing methods. This limits the generalizability of its findings. In addition, I believe that the FHMM generalizations are even more limited than they might appear at first glance.

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  • J. S. Armstrong, 2005. "Commentary by J. Scott Armstrong on Fildes et al.: Generalizing about univariate forecasting methods: further empirical evidence," General Economics and Teaching 0502019, EconWPA.
  • Handle: RePEc:wpa:wuwpgt:0502019
    Note: Type of Document - pdf; pages: 3

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    References listed on IDEAS

    1. Makridakis, Spyros & Hibon, Michele & Lusk, Ed & Belhadjali, Moncef, 1987. "Confidence intervals: An empirical investigation of the series in the M-competition," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 489-508.
    2. Dalrymple, Douglas J., 1975. "Sales forecasting methods and accuracy," Business Horizons, Elsevier, vol. 18(6), pages 69-73, December.
    3. Patricia M. West & Patrick L. Brockett & Linda L. Golden, 1997. "A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice," Marketing Science, INFORMS, pages 370-391.
    4. JS Armstrong & Fred Collopy, 2004. "Causal Forces: Structuring Knowledge for Time-series Extrapolation," General Economics and Teaching 0412003, EconWPA.
    5. Fred Collopy & J. Scott Armstrong, 1992. "Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations," Management Science, INFORMS, vol. 38(10), pages 1394-1414, October.
    6. Alison Hubbard Ashton & Robert H. Ashton, 1985. "Aggregating Subjective Forecasts: Some Empirical Results," Management Science, INFORMS, vol. 31(12), pages 1499-1508, December.
    7. F. Thomas Juster, 1966. "Consumer Buying Intentions and Purchase Probability: An Experiment in Survey Design," NBER Books, National Bureau of Economic Research, Inc, number just66-2, January.
    8. Tyebjee, Tyzoon T., 1987. "Behavioral biases in new product forecasting," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 393-404.
    9. JS Armstrong & Terry Overton, 2005. "Brief vs. Comprehensive Descriptions in Measuring Intentions to Purchase," General Economics and Teaching 0502032, EconWPA.
    10. 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.
    11. Chatfield, Chris, 1993. "Neural networks: Forecasting breakthrough or passing fad?," International Journal of Forecasting, Elsevier, vol. 9(1), pages 1-3, April.
    12. Armstrong, J Scott & Collopy, Fred, 2001. "Identification of Asymmetric Prediction Intervals through Causal Forces," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(4), pages 273-283, July.
    13. 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.
    14. Lawrence, Michael J. & Edmundson, Robert H. & O'Connor, Marcus J., 1985. "An examination of the accuracy of judgmental extrapolation of time series," International Journal of Forecasting, Elsevier, vol. 1(1), pages 25-35.
    15. JS Armstrong & Fred Collopy, 2004. "Integration of Statistical Methods and Judgment for Time Series," General Economics and Teaching 0412024, EconWPA.
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    forecasting; univariate forecasting methods;

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    • A - General Economics and Teaching

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