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A Comparative Study of Methods for Long-Range Market Forecasting

Author

Listed:
  • J. Scott Armstrong

    (University of Pennsylvania)

  • Michael C. Grohman

    (IBM Corporation, Philadelphia)

Abstract

The following hypotheses about long-range market forecasting were examined: H 1 Objective methods provide more accuracy than do subjective methods. H 2 The relative advantage of objective over subjective methods increases as the amount of change in the environment increases. H 3 Causal methods provide more accuracy than do naïve methods. H 4 The relative advantage of causal over naïve 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

  • J. Scott Armstrong & Michael C. Grohman, 1972. "A Comparative Study of Methods for Long-Range Market Forecasting," Management Science, INFORMS, vol. 19(2), pages 211-221, October.
    • Handle: RePEc:inm:ormnsc:v:19:y:1972:i:2:p:211-221
    DOI: 10.1287/mnsc.19.2.211
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    Cited by:

    1. 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.
    2. JS Armstrong, 2004. "Designing and Using Experiential Exercises," General Economics and Teaching 0412022, University Library of Munich, Germany.
    3. Armstrong, J Scott, 1978. "Forecasting with Econometric Methods: Folklore versus Fact," The Journal of Business, University of Chicago Press, vol. 51(4), pages 549-564, October.
    4. Collan, Mikael, 2004. "Giga-Investments: Modelling the Valuation of Very Large Industrial Real Investments," MPRA Paper 4328, University Library of Munich, Germany.
    5. Davis, Donna F. & Mentzer, John T., 2007. "Organizational factors in sales forecasting management," International Journal of Forecasting, Elsevier, vol. 23(3), pages 475-495.
    6. Kott, Alexander & Perconti, Philip, 2018. "Long-term forecasts of military technologies for a 20–30 year horizon: An empirical assessment of accuracy," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 272-279.
    7. Schnaubelt, Matthias, 2019. "A comparison of machine learning model validation schemes for non-stationary time series data," FAU Discussion Papers in Economics 11/2019, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    8. 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.
    9. Suraj Kumar Bhagat, 2025. "Navigating the Challenges of Rainfall Variability: Precipitation Forecasting using Coalesce Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(5), pages 2251-2280, March.

    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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