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Integration of Statistical Methods and Judgment for Time Series

Author

Listed:
  • JS Armstrong

    (The Wharton School - University of Pennsylvania)

  • Fred Collopy

    (Case Western Reserve University)

Abstract

We consider how judgment and statistical methods should be integrated for time-series forecasting. Our review of published empirical research identified 47 studies, all but four published since 1985. Five procedures were identified: revising judgment; combining forecasts; revising extrapolations; rule-based forecasting; and econometric forecasting. This literature suggests that integration generally improves accuracy when the experts have domain knowledge and when significant trends are involved. Integration is valuable to the extent that judgments are used as inputs to the statistical methods, that they contain additional relevant information, and that the integration scheme is well structured. The choice of an integration approach can have a substantial impact on the accuracy of the resulting forecasts. Integration harms accuracy when judgment is biased or its use is unstructured. Equal-weights combining should be regarded as the benchmark and it is especially appropriate where series have high uncertainty or high instability. When the historical data involve high uncertainty or high instability, we recommend revising judgment, revising extrapolations, or combining. When good domain knowledge is available for the future as well as for the past, we recommend rule- based forecasting or econometric methods.

Suggested Citation

  • JS Armstrong & Fred Collopy, 2004. "Integration of Statistical Methods and Judgment for Time Series," General Economics and Teaching 0412024, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpgt:0412024
    Note: Type of Document - pdf; pages: 33
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/get/papers/0412/0412024.pdf
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    References listed on IDEAS

    as
    1. Willemain, Thomas R., 1989. "Graphical adjustment of statistical forecasts," International Journal of Forecasting, Elsevier, vol. 5(2), pages 179-185.
    2. JS Armstrong, 2004. "Relative Accuracy of Judgmental and Extrapolative Methods in Forecasting Annual Earnings," General Economics and Teaching 0412007, University Library of Munich, Germany.
    3. 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.
    4. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    5. Flores, Benito E. & Olson, David L. & Wolfe, Christopher, 1992. "Judgmental adjustment of forecasts: A comparison of methods," International Journal of Forecasting, Elsevier, vol. 7(4), pages 421-433, March.
    6. 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.
    7. Bretschneider, Stuart I. & Gorr, Wilpen L. & Grizzle, Gloria & Klay, Earle, 1989. "Political and organizational influences on the accuracy of forecasting state government revenues," International Journal of Forecasting, Elsevier, vol. 5(3), pages 307-319.
    8. Sanders, NR, 1992. "Accuracy of judgmental forecasts: A comparison," Omega, Elsevier, vol. 20(3), pages 353-364, May.
    9. George Duncan & Wilpen Gorr & Janusz Szczypula, 1993. "Bayesian Forecasting for Seemingly Unrelated Time Series: Application to Local Government Revenue Forecasting," Management Science, INFORMS, vol. 39(3), pages 275-293, March.
    10. JS Armstrong & Fred Collopy, 2004. "Causal Forces: Structuring Knowledge for Time-series Extrapolation," General Economics and Teaching 0412003, University Library of Munich, Germany.
    11. Yokuma, J. Thomas & Armstrong, J. Scott, 1995. "Beyond accuracy: Comparison of criteria used to select forecasting methods," International Journal of Forecasting, Elsevier, vol. 11(4), pages 591-597, December.
    12. MacGregor, Donald & Lichtenstein, Sarah & Slovic, Paul, 1988. "Structuring knowledge retrieval: An analysis of decomposed quantitative judgments," Organizational Behavior and Human Decision Processes, Elsevier, vol. 42(3), pages 303-323, December.
    13. Davis, Fred D. & Lohse, Gerald L. & Kottemann, Jeffrey E., 1994. "Harmful effects of seemingly helpful information on forecasts of stock earnings," Journal of Economic Psychology, Elsevier, vol. 15(2), pages 253-267, June.
    14. 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.
    15. Goodwin, P., 1996. "Statistical correction of judgmental point forecasts and decisions," Omega, Elsevier, vol. 24(5), pages 551-559, October.
    16. 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.
    17. Collopy, Fred & Armstrong, J. Scott, 1992. "Expert opinions about extrapolation and the mystery of the overlooked discontinuities," International Journal of Forecasting, Elsevier, vol. 8(4), pages 575-582, December.
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    Cited by:

    1. J. S. Armstrong, 2005. "Review of: Predicting Presidential Elections and Other Things," General Economics and Teaching 0502016, University Library of Munich, Germany.
    2. Armstrong, J. Scott & Collopy, Fred & Yokum, J. Thomas, 2005. "Decomposition by causal forces: a procedure for forecasting complex time series," International Journal of Forecasting, Elsevier, vol. 21(1), pages 25-36.
    3. JS Armstrong, 2004. "Should We Redesign Forecasting Competitions?," General Economics and Teaching 0412001, University Library of Munich, Germany.
    4. JS Armstrong, 2004. "Forecasting for Environmental Decision Making," General Economics and Teaching 0412023, University Library of Munich, Germany.
    5. J. S. Armstrong & R. Brodie, 2005. "Forecasting for Marketing," General Economics and Teaching 0502018, University Library of Munich, Germany.
    6. J. Scott Armstrong & Kesten C. Green, 2005. "Demand Forecasting: Evidence-based Methods," Monash Econometrics and Business Statistics Working Papers 24/05, Monash University, Department of Econometrics and Business Statistics.

    More about this item

    Keywords

    statistical methods; statistics; time series; forecasting; empirical research;

    JEL classification:

    • A - General Economics and Teaching

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