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Reference Class Forecasting: Resolving Its Challenge to Statistical Modeling

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  • Robert F. Bordley

Abstract

Statisticians generally consider statistical modeling superior (or at least a useful supplement) to experience-based intuition for estimating the outputs of a complex system. But recent psychological research has led to an enhancement of experience-based intuition known as reference class forecasting. The reference class forecasting approach has been championed as a superior alternative to statistical modeling and is already well-regarded in the planning community. This presents a challenge to statistical modeling. To address this challenge, this article uses a Bayesian approach for combining the reference class forecast and the model-based forecast. The Bayesian prior is informed by the reference class information. A likelihood function was constructed to reflect the model's information. This approach was used to estimate healthcare costs under a voluntary employee benefit association (VEBA). The resulting Bayesian posterior forecast had lower variance (and lower forecast error) than either the model-based forecast or the reference-class forecast.

Suggested Citation

  • Robert F. Bordley, 2014. "Reference Class Forecasting: Resolving Its Challenge to Statistical Modeling," The American Statistician, Taylor & Francis Journals, vol. 68(4), pages 221-229, November.
  • Handle: RePEc:taf:amstat:v:68:y:2014:i:4:p:221-229
    DOI: 10.1080/00031305.2014.937544
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    Cited by:

    1. Flyvbjerg, Bent & Bester, Dirk W., 2021. "The Cost-Benefit Fallacy: Why Cost-Benefit Analysis Is Broken and How to Fix It," Journal of Benefit-Cost Analysis, Cambridge University Press, vol. 12(3), pages 395-419, October.
    2. Flyvbjerg, Bent & Ansar, Atif & Budzier, Alexander & Buhl, Søren & Cantarelli, Chantal & Garbuio, Massimo & Glenting, Carsten & Holm, Mette Skamris & Lovallo, Dan & Lunn, Daniel & Molin, Eric & Rønnes, 2018. "Five things you should know about cost overrun," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 174-190.
    3. Robert F. Bordley, 2023. "Lessons for Decision-Analysis Practice from the Automotive Industry," Interfaces, INFORMS, vol. 53(3), pages 240-246, May.

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