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Combining probability forecasts

Author

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  • Roopesh Ranjan
  • Tilmann Gneiting

Abstract

Linear pooling is by far the most popular method for combining probability forecasts. However, any non-trivial weighted average of two or more distinct, calibrated probability forecasts is necessarily uncalibrated and lacks sharpness. In view of this, linear pooling requires recalibration, even in the ideal case in which the individual forecasts are calibrated. Towards this end, we propose a beta-transformed linear opinion pool for the aggregation of probability forecasts from distinct, calibrated or uncalibrated sources. The method fits an optimal non-linearly recalibrated forecast combination, by compositing a beta transform and the traditional linear opinion pool. The technique is illustrated in a simulation example and in a case-study on statistical and National Weather Service probability of precipitation forecasts. Copyright (c) 2010 Royal Statistical Society.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:jorssb:v:72:y:2010:i:1:p:71-91
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    References listed on IDEAS

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    Cited by:

    1. repec:eee:reensy:v:108:y:2012:i:c:p:77-89 is not listed on IDEAS
    2. Pirschel, Inske, 2016. "Forecasting euro area recessions in real-time," Kiel Working Papers 2020, Kiel Institute for the World Economy (IfW).
    3. Satopää, Ville A. & Baron, Jonathan & Foster, Dean P. & Mellers, Barbara A. & Tetlock, Philip E. & Ungar, Lyle H., 2014. "Combining multiple probability predictions using a simple logit model," International Journal of Forecasting, Elsevier, vol. 30(2), pages 344-356.
    4. Haben, Stephen & Giasemidis, Georgios, 2016. "A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1017-1022.
    5. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
    6. repec:gam:jecnmx:v:4:y:2016:i:1:p:17:d:65855 is not listed on IDEAS
    7. Tommaso Proietti & Martyna Marczak & Gianluigi Mazzi, 2017. "Euromind‐ D : A Density Estimate of Monthly Gross Domestic Product for the Euro Area," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 683-703, April.
    8. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
    9. Roberto Casarin & Federico Bassetti & Francesco Ravazzolo, 2015. "Bayesian Nonparametric Calibration and Combination of Predictive Distributions," Working Papers 2015:04, Department of Economics, University of Venice "Ca' Foscari".
    10. repec:eee:reensy:v:112:y:2013:i:c:p:109-119 is not listed on IDEAS
    11. Lahiri Kajal & Yang Liu, 2016. "A non-linear forecast combination procedure for binary outcomes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 421-440, September.
    12. Timo Henckel & Shaun Vahey & Liz Wakerly, 2011. "Probabilistic Interest Rate Setting With A Shadow Board: A Description Of The Pilot Project," CAMA Working Papers 2011-27, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    13. repec:bla:jorssb:v:79:y:2017:i:4:p:959-1035 is not listed on IDEAS
    14. Enrique Moral-Benito, 2015. "Model Averaging In Economics: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 46-75, February.
    15. Roberto Casarin & Giulia Mantoan & Francesco Ravazzolo, 2016. "Bayesian Calibration of Generalized Pools of Predictive Distributions," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-24, March.
    16. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, Elsevier.
    17. P. Schanbacher, 2014. "Measuring and adjusting for overconfidence," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 37(2), pages 423-452, October.

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