- Statist. Sci.
- Volume 32, Number 1 (2017), 106-127.
Forecaster’s Dilemma: Extreme Events and Forecast Evaluation
In public discussions of the quality of forecasts, attention typically focuses on the predictive performance in cases of extreme events. However, the restriction of conventional forecast evaluation methods to subsets of extreme observations has unexpected and undesired effects, and is bound to discredit skillful forecasts when the signal-to-noise ratio in the data generating process is low. Conditioning on outcomes is incompatible with the theoretical assumptions of established forecast evaluation methods, thereby confronting forecasters with what we refer to as the forecaster’s dilemma. For probabilistic forecasts, proper weighted scoring rules have been proposed as decision-theoretically justifiable alternatives for forecast evaluation with an emphasis on extreme events. Using theoretical arguments, simulation experiments and a real data study on probabilistic forecasts of U.S. inflation and gross domestic product (GDP) growth, we illustrate and discuss the forecaster’s dilemma along with potential remedies.
Statist. Sci. Volume 32, Number 1 (2017), 106-127.
First available in Project Euclid: 6 April 2017
Permanent link to this document
Digital Object Identifier
Lerch, Sebastian; Thorarinsdottir, Thordis L.; Ravazzolo, Francesco; Gneiting, Tilmann. Forecaster’s Dilemma: Extreme Events and Forecast Evaluation. Statist. Sci. 32 (2017), no. 1, 106--127. doi:10.1214/16-STS588. https://projecteuclid.org/euclid.ss/1491465630
- Additional figures and tables. We provide further figures for Sections 3.3 and 4.3 and a version of Table 1 with direct links to the original media sources.