IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v59y2013i7p1594-1611.html
   My bibliography  Save this article

Is It Better to Average Probabilities or Quantiles?

Author

Listed:
  • Kenneth C. Lichtendahl

    (Darden School of Business, University of Virginia, Charlottesville, Virginia 22906)

  • Yael Grushka-Cockayne

    (Darden School of Business, University of Virginia, Charlottesville, Virginia 22906)

  • Robert L. Winkler

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

Abstract

We consider two ways to aggregate expert opinions using simple averages: averaging probabilities and averaging quantiles. We examine analytical properties of these forecasts and compare their ability to harness the wisdom of the crowd. In terms of location, the two average forecasts have the same mean. The average quantile forecast is always sharper: it has lower variance than the average probability forecast. Even when the average probability forecast is overconfident, the shape of the average quantile forecast still offers the possibility of a better forecast. Using probability forecasts for gross domestic product growth and inflation from the Survey of Professional Forecasters, we present evidence that both when the average probability forecast is overconfident and when it is underconfident, it is outperformed by the average quantile forecast. Our results show that averaging quantiles is a viable alternative and indicate some conditions under which it is likely to be more useful than averaging probabilities. This paper was accepted by Peter Wakker, decision analysis.

Suggested Citation

  • Kenneth C. Lichtendahl & Yael Grushka-Cockayne & Robert L. Winkler, 2013. "Is It Better to Average Probabilities or Quantiles?," Management Science, INFORMS, vol. 59(7), pages 1594-1611, July.
  • Handle: RePEc:inm:ormnsc:v:59:y:2013:i:7:p:1594-1611
    DOI: 10.1287/mnsc.1120.1667
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.1120.1667
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.1120.1667?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Clements, Michael P., 2010. "Explanations of the inconsistencies in survey respondents' forecasts," European Economic Review, Elsevier, vol. 54(4), pages 536-549, May.
    2. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
    3. James E. Matheson & Robert L. Winkler, 1976. "Scoring Rules for Continuous Probability Distributions," Management Science, INFORMS, vol. 22(10), pages 1087-1096, June.
    4. Kenneth Wallis, 2011. "Combining forecasts - forty years later," Applied Financial Economics, Taylor & Francis Journals, vol. 21(1-2), pages 33-41.
    5. Richard P. Larrick & Jack B. Soll, 2006. "Erratum--Intuitions About Combining Opinions: Misappreciation of the Averaging Principle," Management Science, INFORMS, vol. 52(2), pages 309-310, February.
    6. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    7. Emir Shuford & Arthur Albert & H. Edward Massengill, 1966. "Admissible probability measurement procedures," Psychometrika, Springer;The Psychometric Society, vol. 31(2), pages 125-145, June.
    8. Clemon, Robert T & Winkler, Robert L, 1986. "Combining Economic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 39-46, January.
    9. Roopesh Ranjan & Tilmann Gneiting, 2010. "Combining probability forecasts," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 71-91, January.
    10. Stephen C. Hora, 2004. "Probability Judgments for Continuous Quantities: Linear Combinations and Calibration," Management Science, INFORMS, vol. 50(5), pages 597-604, May.
    11. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    12. R. Winkler & Javier Muñoz & José Cervera & José Bernardo & Gail Blattenberger & Joseph Kadane & Dennis Lindley & Allan Murphy & Robert Oliver & David Ríos-Insua, 1996. "Scoring rules and the evaluation of probabilities," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 5(1), pages 1-60, June.
    13. Stephen C. Hora, 2010. "An Analytic Method for Evaluating the Performance of Aggregation Rules for Probability Densities," Operations Research, INFORMS, vol. 58(5), pages 1440-1449, October.
    14. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    15. Peter A. Morris, 1977. "Combining Expert Judgments: A Bayesian Approach," Management Science, INFORMS, vol. 23(7), pages 679-693, March.
    16. David V. Budescu & Ning Du, 2007. "Coherence and Consistency of Investors' Probability Judgments," Management Science, INFORMS, vol. 53(11), pages 1731-1744, November.
    17. James Mitchell & Kenneth F. Wallis, 2011. "Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 1023-1040, September.
    18. Ali E. Abbas & David V. Budescu & Hsiu-Ting Yu & Ryan Haggerty, 2008. "A Comparison of Two Probability Encoding Methods: Fixed Probability vs. Fixed Variable Values," Decision Analysis, INFORMS, vol. 5(4), pages 190-202, December.
    19. Richard P. Larrick & Jack B. Soll, 2006. "Intuitions About Combining Opinions: Misappreciation of the Averaging Principle," Management Science, INFORMS, vol. 52(1), pages 111-127, January.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    3. Robert L. Winkler & Yael Grushka-Cockayne & Kenneth C. Lichtendahl Jr. & Victor Richmond R. Jose, 2019. "Probability Forecasts and Their Combination: A Research Perspective," Decision Analysis, INFORMS, vol. 16(4), pages 239-260, December.
    4. Victor Richmond R. Jose & Yael Grushka-Cockayne & Kenneth C. Lichtendahl, 2014. "Trimmed Opinion Pools and the Crowd's Calibration Problem," Management Science, INFORMS, vol. 60(2), pages 463-475, February.
    5. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.
    6. Taylor, James W. & Taylor, Kathryn S., 2023. "Combining probabilistic forecasts of COVID-19 mortality in the United States," European Journal of Operational Research, Elsevier, vol. 304(1), pages 25-41.
    7. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    8. Tsyplakov, Alexander, 2013. "Evaluation of Probabilistic Forecasts: Proper Scoring Rules and Moments," MPRA Paper 45186, University Library of Munich, Germany.
    9. Yael Grushka-Cockayne & Kenneth C. Lichtendahl Jr. & Victor Richmond R. Jose & Robert L. Winkler, 2017. "Quantile Evaluation, Sensitivity to Bracketing, and Sharing Business Payoffs," Operations Research, INFORMS, vol. 65(3), pages 712-728, June.
    10. Stephen C. Hora & Benjamin R. Fransen & Natasha Hawkins & Irving Susel, 2013. "Median Aggregation of Distribution Functions," Decision Analysis, INFORMS, vol. 10(4), pages 279-291, December.
    11. Marcus P. A. Cobb, 2020. "Aggregate density forecasting from disaggregate components using Bayesian VARs," Empirical Economics, Springer, vol. 58(1), pages 287-312, January.
    12. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    13. Stephen Hora & Erim Kardeş, 2015. "Calibration, sharpness and the weighting of experts in a linear opinion pool," Annals of Operations Research, Springer, vol. 229(1), pages 429-450, June.
    14. Werner Ehm & Tilmann Gneiting & Alexander Jordan & Fabian Krüger, 2016. "Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 505-562, June.
    15. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.
    16. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207, April.
    17. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
    18. Carol Alexander & Michael Coulon & Yang Han & Xiaochun Meng, 2021. "Evaluating the Discrimination Ability of Proper Multivariate Scoring Rules," Papers 2101.12693, arXiv.org.
    19. Markus Eyting & Patrick Schmidt, 2019. "Belief Elicitation with Multiple Point Predictions," Working Papers 1818, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, revised 16 Nov 2020.
    20. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207.

    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:inm:ormnsc:v:59:y:2013:i:7:p:1594-1611. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.