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Bias-corrected score decomposition for generalized quantiles

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  • W. Ehm
  • E. Y. Ovcharov

Abstract

SummaryDecompositions of the score of a forecast represent useful tools for assessing its performance. We consider local score decompositions permitting detailed forecast assessments across a spectrum of conditions of interest. We derive corrections to the bias of the decomposition components in the framework of point forecasts of quantile-type functionals, and illustrate their performance by simulation. Related bias corrections have thus far only been known for squared error criteria.

Suggested Citation

  • W. Ehm & E. Y. Ovcharov, 2017. "Bias-corrected score decomposition for generalized quantiles," Biometrika, Biometrika Trust, vol. 104(2), pages 473-480.
    • Handle: RePEc:oup:biomet:v:104:y:2017:i:2:p:473-480.
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    File URL: http://hdl.handle.net/10.1093/biomet/asx004
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    Cited by:

    1. Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.

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