IDEAS home Printed from https://ideas.repec.org/p/wpa/wuwpgt/0502025.html
   My bibliography  Save this paper

Forecasting by Extrapolation: Conclusions from 25 Years of Research

Author

Listed:
  • J. S. Armstrong

    (The Wharton School)

Abstract

Sophisticated extrapolation techniques have had a negligible payoff for accuracy in forecasting. As a result, major changes are proposed for the allocation of the funds for- future research on extrapolation. Meanwhile, simple methods and the combination of forecasts are recommended.

Suggested Citation

  • J. S. Armstrong, 2005. "Forecasting by Extrapolation: Conclusions from 25 Years of Research," General Economics and Teaching 0502025, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpgt:0502025
    Note: Type of Document - pdf; pages: 13
    as

    Download full text from publisher

    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/get/papers/0502/0502025.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:asg:wpaper:1002 is not listed on IDEAS
    2. Jerzy Witold Wiśniewski, 2021. "Forecasting in Small Business Management," Risks, MDPI, vol. 9(4), pages 1-17, April.
    3. Li, Shuying & Garces, Edwin & Daim, Tugrul, 2019. "Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    4. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    5. Arvydas Jadevicius & Brian Sloan & Andrew Brown, 2013. "Property Market Modelling and Forecasting: A Case for Simplicity," ERES eres2013_10, European Real Estate Society (ERES).
    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. Kweon, Young-Jun, 2010. "Data-driven reduction targets for a highway safety plan," Transport Policy, Elsevier, vol. 17(4), pages 230-239, August.
    8. JS Armstrong, 2004. "Research on Forecasting: A Quarter-Century Review, 1960-1984," General Economics and Teaching 0412006, University Library of Munich, Germany.
    9. Diamantopoulos, Adamantios & Winklhofer, Heidi, 2003. "Export sales forecasting by UK firms: Technique utilization and impact on forecast accuracy," Journal of Business Research, Elsevier, vol. 56(1), pages 45-54, January.
    10. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    11. Danese, Pamela & Kalchschmidt, Matteo, 2011. "The impact of forecasting on companies' performance: Analysis in a multivariate setting," International Journal of Production Economics, Elsevier, vol. 133(1), pages 458-469, September.
    12. Huang, Chun-Yao, 2012. "To model, or not to model: Forecasting for customer prioritization," International Journal of Forecasting, Elsevier, vol. 28(2), pages 497-506.
    13. JS Armstrong, 2004. "Forecasting for Environmental Decision Making," General Economics and Teaching 0412023, University Library of Munich, Germany.
    14. Jaganathan, Srihari & Prakash, P.K.S., 2020. "A combination-based forecasting method for the M4-competition," International Journal of Forecasting, Elsevier, vol. 36(1), pages 98-104.
    15. Ashish Sood & Gareth M. James & Gerard J. Tellis, 2009. "Functional Regression: A New Model for Predicting Market Penetration of New Products," Marketing Science, INFORMS, vol. 28(1), pages 36-51, 01-02.
    16. Pathak, Pankaj & Srivastava, Rajiv Ranjan & Ojasvi,, 2017. "Assessment of legislation and practices for the sustainable management of waste electrical and electronic equipment in India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 220-232.
    17. Ashish Sood & Gareth M. James & Gerard J. Tellis & Ji Zhu, 2012. "Predicting the Path of Technological Innovation: SAW vs. Moore, Bass, Gompertz, and Kryder," Marketing Science, INFORMS, vol. 31(6), pages 964-979, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    2. Colin Small & J. Eric Bickel, 2022. "Model Complexity and Accuracy: A COVID-19 Case Study," Decision Analysis, INFORMS, vol. 19(4), pages 354-383, December.
    3. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    4. Ashish Sood & Gareth M. James & Gerard J. Tellis, 2009. "Functional Regression: A New Model for Predicting Market Penetration of New Products," Marketing Science, INFORMS, vol. 28(1), pages 36-51, 01-02.
    5. Armstrong, J. Scott, 1983. "Strategic Planning and Forecasting Fundamentals," MPRA Paper 81682, University Library of Munich, Germany.
    6. Scott Moss & Bruce Edmonds & Steve Wallis, 1997. "Validation and Verification of Computational Models with Multiple Cognitive Agents," Discussion Papers 97-25, Manchester Metropolitan University, Centre for Policy Modelling.
    7. repec:cup:judgdm:v:10:y:2015:i:5:p:469-478 is not listed on IDEAS
    8. Simon Glöser-Chahoud & Johannes Hartwig & I. David Wheat & Martin Faulstich, 2016. "The cobweb theorem and delays in adjusting supply in metals' markets," System Dynamics Review, System Dynamics Society, vol. 32(3-4), pages 279-308, July.
    9. Afshin Amiraslany & Hari S. Luitel & Gerry J. Mahar, 2019. "Structural Breaks, Biased Estimations, and Forecast Errors in a GDP Series of Canada versus the United States," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 25(2), pages 235-244, May.
    10. Crystal C. Hall & Daniel M. Oppenheimer, 2015. "Error Parsing: An alternative method of implementing social judgment theory," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(5), pages 469-478, September.

    More about this item

    Keywords

    forecasting; methods; extrapolation;
    All these keywords.

    JEL classification:

    • A - General Economics and Teaching

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpgt:0502025. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: EconWPA (email available below). General contact details of provider: https://econwpa.ub.uni-muenchen.de .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.