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A Comparative Study of Methods for Long-Range Market Forecasting

Author

Listed:
  • JS Armstrong

    (The Wharton School - University of Pennsylvania)

  • Michael C. Grohman

    (IBM Corporation - Philadelphia)

Abstract

The following hypotheses about long-range market forecasting were examined: Hl Objective methods provide more accuracy than do subjective methods. H2 The relative advantage of objective over subjective methods increases as the amount of change in the environment increases. H3 Causal methods provide more accuracy than do naive methods. H4 The relative advantage of causal over naive methods increases as the amount of change in the environment increases. Support for these hypotheses was then obtained from the literature and from a study of a single market. The study used three different models to make ex ante forecasts of the U.S. air travel market from 1963 through 1968. These hypotheses imply that econometric methods are more accurate for long range market forecasting than are the major alternatives, expert judgment and extrapolation, and that the relative superiority of econometric methods increases as the time span of the forecast increases.

Suggested Citation

  • JS Armstrong & Michael C. Grohman, 2004. "A Comparative Study of Methods for Long-Range Market Forecasting," General Economics and Teaching 0412010, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpgt:0412010
    Note: Type of Document - pdf; pages: 12
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/get/papers/0412/0412010.pdf
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    References listed on IDEAS

    as
    1. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters,in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46 National Bureau of Economic Research, Inc.
    2. N/A, 1962. "Summary," National Institute Economic Review, National Institute of Economic and Social Research, vol. 19(1), pages 3-3, February.
    3. N/A, 1962. "Summary," National Institute Economic Review, National Institute of Economic and Social Research, vol. 20(1), pages 3-3, May.
    4. N/A, 1962. "Summary," National Institute Economic Review, National Institute of Economic and Social Research, vol. 21(1), pages 3-3, August.
    5. J. G. Cragg & Burton G. Malkiel, 1968. "The Consensus And Accuracy Of Some Predictions Of The Growth Of Corporate Earnings," Journal of Finance, American Finance Association, vol. 23(1), pages 67-84, March.
    6. N/A, 1962. "Summary," National Institute Economic Review, National Institute of Economic and Social Research, vol. 22(1), pages 3-3, November.
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    Citations

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    Cited by:

    1. JS Armstrong, 2004. "Designing and Using Experiential Exercises," General Economics and Teaching 0412022, University Library of Munich, Germany.
    2. Armstrong, J. Scott, 1978. "Forecasting with Econometric Methods: Folklore Versus Fact," MPRA Paper 81672, University Library of Munich, Germany.
    3. Collan, Mikael, 2004. "Giga-Investments: Modelling the Valuation of Very Large Industrial Real Investments," MPRA Paper 4328, University Library of Munich, Germany.
    4. Davis, Donna F. & Mentzer, John T., 2007. "Organizational factors in sales forecasting management," International Journal of Forecasting, Elsevier, vol. 23(3), pages 475-495.
    5. Tessier, Thomas H. & Armstrong, J. Scott, 2015. "Decomposition of time-series by level and change," Journal of Business Research, Elsevier, vol. 68(8), pages 1755-1758.
    6. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.

    More about this item

    Keywords

    long-range market forecasting; forecasting methods; forecasting;

    JEL classification:

    • A - General Economics and Teaching

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