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Relative performance of methods for forecasting special events

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  • Nikolopoulos, Konstantinos
  • Litsa, Akrivi
  • Petropoulos, Fotios
  • Bougioukos, Vasileios
  • Khammash, Marwan

Abstract

Forecasting special events such as conflicts and epidemics is challenging because of their nature and the limited amount of historical information from which a reference base can be built. This study evaluates the performances of structured analogies, the Delphi method and interaction groups in forecasting the impact of such events. The empirical evidence reveals that the use of structured analogies leads to an average forecasting accuracy improvement of 8.4% compared to unaided judgment. This improvement in accuracy is greater when the use of structured analogies is accompanied by an increase in the level of expertise, the use of more analogies, the relevance of these analogies, and the introduction of pooling analogies through interaction within experts. Furthermore, the results from group judgmental forecasting approaches were very promising; the Delphi method and interaction groups improved accuracy by 27.0% and 54.4%, respectively.

Suggested Citation

  • Nikolopoulos, Konstantinos & Litsa, Akrivi & Petropoulos, Fotios & Bougioukos, Vasileios & Khammash, Marwan, 2015. "Relative performance of methods for forecasting special events," Journal of Business Research, Elsevier, vol. 68(8), pages 1785-1791.
    • Handle: RePEc:eee:jbrese:v:68:y:2015:i:8:p:1785-1791
    DOI: 10.1016/j.jbusres.2015.03.037
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    References listed on IDEAS

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    1. Armstrong, J. Scott, 2007. "Significance tests harm progress in forecasting," International Journal of Forecasting, Elsevier, vol. 23(2), pages 321-327.
    2. Lee, Wing Yee & Goodwin, Paul & Fildes, Robert & Nikolopoulos, Konstantinos & Lawrence, Michael, 2007. "Providing support for the use of analogies in demand forecasting tasks," International Journal of Forecasting, Elsevier, vol. 23(3), pages 377-390.
    3. Goodwin, Paul, 2007. "Should we be using significance tests in forecasting research?," International Journal of Forecasting, Elsevier, vol. 23(2), pages 333-334.
    4. Graefe, Andreas & Armstrong, J. Scott, 2011. "Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task," International Journal of Forecasting, Elsevier, vol. 27(1), pages 183-195, January.
    5. Green, Kesten C. & Armstrong, J. Scott, 2007. "Structured analogies for forecasting," International Journal of Forecasting, Elsevier, vol. 23(3), pages 365-376.
    6. Nicolas Savio & Konstantinos Nikolopoulos, 2010. "Forecasting the Effectiveness of Policy Implementation Strategies," International Journal of Public Administration, Taylor & Francis Journals, vol. 33(2), pages 88-97.
    7. Simpson, David & Walker, James, 1987. "Extending cost-benefit analysis for energy investment choices," Energy Policy, Elsevier, vol. 15(3), pages 217-227, June.
    8. Armstrong, J. Scott, 2007. "Statistical significance tests are unnecessary even when properly done and properly interpreted: Reply to commentaries," International Journal of Forecasting, Elsevier, vol. 23(2), pages 335-336.
    9. David M. Boje & J. Keith Murnighan, 1982. "Group Confidence Pressures in Iterative Decisions," Management Science, INFORMS, vol. 28(10), pages 1187-1196, October.
    10. Savio, Nicolas D. & Nikolopoulos, Konstantinos, 2013. "A strategic forecasting framework for governmental decision-making and planning," International Journal of Forecasting, Elsevier, vol. 29(2), pages 311-321.
    11. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    12. Simon, Herbert A, 1979. "Rational Decision Making in Business Organizations," American Economic Review, American Economic Association, vol. 69(4), pages 493-513, September.
    13. Kesten C. Green & J. Scott Armstrong, 2004. "Value of Expertise For Forecasting Decisions in Conflicts," Monash Econometrics and Business Statistics Working Papers 27/04, Monash University, Department of Econometrics and Business Statistics.
    14. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    15. Zellner, Arnold, 2007. "Philosophy and objectives of econometrics," Journal of Econometrics, Elsevier, vol. 136(2), pages 331-339, February.
    16. Nikolopoulos, K. & Goodwin, P. & Patelis, A. & Assimakopoulos, V., 2007. "Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches," European Journal of Operational Research, Elsevier, vol. 180(1), pages 354-368, July.
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    Cited by:

    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. Konstantinos Nikolopoulos & Waleed S. Alghassab & Konstantia Litsiou & Stelios Sapountzis, 2019. "Long-Term Economic Forecasting with Structured Analogies and Interaction Groups," Working Papers 19018, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    3. Katsagounos, Ilias & Thomakos, Dimitrios D. & Litsiou, Konstantia & Nikolopoulos, Konstantinos, 2021. "Superforecasting reality check: Evidence from a small pool of experts and expedited identification," European Journal of Operational Research, Elsevier, vol. 289(1), pages 107-117.
    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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos, 2022. "Relative performance of judgmental methods for forecasting the success of megaprojects," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1185-1196.
    7. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    8. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).
    9. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    10. Lu, Emiao & Handl, Julia & Xu, Dong-ling, 2018. "Determining analogies based on the integration of multiple information sources," International Journal of Forecasting, Elsevier, vol. 34(3), pages 507-528.

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