IDEAS home Printed from https://ideas.repec.org/a/wly/fufsci/v6y2024i4ne189.html
   My bibliography  Save this article

Scoring rules and performance, new analysis of expert judgment data

Author

Listed:
  • Gabriela F. Nane
  • Roger M. Cooke

Abstract

A review of scoring rules highlights the distinction between rewarding honesty and rewarding quality. This motivates the introduction of a scale‐invariant version of the Continuous Ranked Probability Score (CRPS) which enables statistical accuracy (SA) testing based on an exact rather than an asymptotic distribution of the density of convolutions. A recent data set of 6761 expert probabilistic forecasts for questions for which the actual values are known is used to compare performance. New insights include that (a) variance due to assessed variables dominates variance due to experts, (b) performance on mean absolute percentage error (MAPE) is weakly related to SA (c) scale‐invariant CRPS combinations compete with the Classical Model (CM) on SA and MAPE, and (d) CRPS is more forgiving with regard to SA than the CM as CRPS is insensitive to location bias.

Suggested Citation

  • Gabriela F. Nane & Roger M. Cooke, 2024. "Scoring rules and performance, new analysis of expert judgment data," Futures & Foresight Science, John Wiley & Sons, vol. 6(4), December.
    • Handle: RePEc:wly:fufsci:v:6:y:2024:i:4:n:e189
    DOI: 10.1002/ffo2.189
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/ffo2.189
    Download Restriction: no
    File URL: https://libkey.io/10.1002/ffo2.189?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. Cooke, Roger M. & Marti, Deniz & Mazzuchi, Thomas, 2021. "Expert forecasting with and without uncertainty quantification and weighting: What do the data say?," International Journal of Forecasting, Elsevier, vol. 37(1), pages 378-387.
    2. Alexander Dawid & Monica Musio, 2014. "Theory and applications of proper scoring rules," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 169-183, August.
    3. Peter A. Morris, 1974. "Decision Analysis Expert Use," Management Science, INFORMS, vol. 20(9), pages 1233-1241, May.
    4. Emir Shuford & Arthur Albert & H. Edward Massengill, 1966. "Admissible probability measurement procedures," Psychometrika, Springer;The Psychometric Society, vol. 31(2), pages 125-145, June.
    5. Arthur Carvalho, 2016. "An Overview of Applications of Proper Scoring Rules," Decision Analysis, INFORMS, vol. 13(4), pages 223-242, December.
    6. 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.
    7. 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.
    8. Peter A. Morris, 1977. "Combining Expert Judgments: A Bayesian Approach," Management Science, INFORMS, vol. 23(7), pages 679-693, March.
    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. 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.
    2. 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.
    3. Geweke, John & Amisano, Gianni, 2011. "Optimal prediction pools," Journal of Econometrics, Elsevier, vol. 164(1), pages 130-141, September.
    4. Enrique Moral-Benito, 2015. "Model Averaging In Economics: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 46-75, February.
    5. Anne Sofie Jore & James Mitchell & Shaun P. Vahey, 2010. "Combining forecast densities from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 621-634.
    6. Tino Werner, 2022. "Elicitability of Instance and Object Ranking," Decision Analysis, INFORMS, vol. 19(2), pages 123-140, June.
    7. 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.
    8. Edgar C. Merkle & Robert Hartman, 2018. "Weighted Brier score decompositions for topically heterogenous forecasting tournaments," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(2), pages 185-201, March.
    9. 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.
    10. repec:cup:judgdm:v:13:y:2018:i:2:p:185-201 is not listed on IDEAS
    11. Merkle, Edgar C. & Steyvers, Mark & Mellers, Barbara & Tetlock, Philip E., 2017. "A neglected dimension of good forecasting judgment: The questions we choose also matter," International Journal of Forecasting, Elsevier, vol. 33(4), pages 817-832.
    12. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    13. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    14. Ali Mosleh & George Apostolakis, 1986. "The Assessment of Probability Distributions from Expert Opinions with an Application to Seismic Fragility Curves," Risk Analysis, John Wiley & Sons, vol. 6(4), pages 447-461, December.
    15. Thibault Gajdos & Jean-Christophe Vergnaud, 2013. "Decisions with conflicting and imprecise information," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 41(2), pages 427-452, July.
    16. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    17. Tilmann Gneiting & Larissa Stanberry & Eric Grimit & Leonhard Held & Nicholas Johnson, 2008. "Rejoinder on: Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(2), pages 256-264, August.
    18. Norde, Henk & Voorneveld, Mark, 2019. "Feasible best-response correspondences and quadratic scoring rules," SSE Working Paper Series in Economics 2019:2, Stockholm School of Economics.
    19. Sigrist, Fabio & Leuenberger, Nicola, 2023. "Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1390-1406.
    20. Mike Ludkovski & Glen Swindle & Eric Grannan, 2022. "Large Scale Probabilistic Simulation of Renewables Production," Papers 2205.04736, arXiv.org.
    21. Roberto Leon-Gonzalez & Blessings Majoni, 2023. "Exact Likelihood for Inverse Gamma Stochastic Volatility Models," GRIPS Discussion Papers 23-07, National Graduate Institute for Policy Studies.

    More about this item

    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:wly:fufsci:v:6:y:2024:i:4:n:e189. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)2573-5152 .

    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.