Tail calibration of probabilistic forecasts

Sam Allen (), Jonathan Koh, Johan Segers and Johanna Ziegel
Additional contact information
Sam Allen: ETH Zurich
Jonathan Koh: University of Bern
Johan Segers: Université catholique de Louvain, LIDAM/ISBA, Belgium
Johanna Ziegel: ETH Zurich

No 2024018, LIDAM Discussion Papers ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: Probabilistic forecasts comprehensively describe the uncertainty in the unknown future outcome, making them essential for decision making and risk management. While several methods have been introduced to evaluate probabilistic forecasts, existing evaluation techniques are ill-suited to the evaluation of tail properties of such forecasts. However, these tail properties are often of particular interest to forecast users due to the severe impacts caused by extreme outcomes. In this work, we introduce a general notion of tail calibration for probabilistic forecasts, which allows forecasters to assess the reliability of their predictions for extreme outcomes. We study the relationships between tail calibration and standard notions of forecast calibration, and discuss connections to peaks-over-threshold models in extreme value theory. Diagnostic tools are introduced and applied in a case study on European precipitation forecasts.

Keywords: Extreme event; proper scoring rule; forecast evaluation; tail calibration diagnostic plot; precipitation forecast (search for similar items in EconPapers)
Pages: 40
Date: 2024-07-04
New Economics Papers: this item is included in nep-ecm, nep-for and nep-rmg
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Citations: View citations in EconPapers (1)

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