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Forecasting Conditional Probabilities of Binary Outcomes under Misspecification

Author

Listed:
  • Graham Elliott

    (University of California, San Diego)

  • Dalia Ghanem

    (University of California, Davis, and Giannini Foundation)

  • Fabian Krüger

    (Heidelberg Institute for Theoretical Studies (HITS))

Abstract

We consider constructing probability forecasts from a parametric binary choice model under a large family of loss functions (“scoring rules”). Scoring rules are weighted averages over the utilities that heterogeneous decision makers derive from a publicly announced forecast (Schervish, 1989). Using analytical and numerical examples, we illustrate howdifferent scoring rules yield asymptotically identical results if the model is correctly specified. Under misspecification, the choice of scoring rule may be inconsequential under restrictive symmetry conditions on the data-generating process. If these conditions are violated, typically the choice of a scoring rule favors some decision makers over others.

Suggested Citation

  • Graham Elliott & Dalia Ghanem & Fabian Krüger, 2016. "Forecasting Conditional Probabilities of Binary Outcomes under Misspecification," The Review of Economics and Statistics, MIT Press, vol. 98(4), pages 742-755, October.
    • Handle: RePEc:tpr:restat:v:98:y:2016:i:4:p:742-755
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    File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/REST_a_00564
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    Citations

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    Cited by:

    1. 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.

    More about this item

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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