Forecaster's Dilemma: Extreme Events and Forecast Evaluation
Forecaster's Dilemma: Extreme Events and Forecast Evaluation
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DOI：
10.1214/16-sts588
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发表时间：
2017-02-01
影响因子：
5.7
通讯作者：
Gneiting, Tilmann
中科院分区：
文献类型：
--
作者：
Lerch, Sebastian;Thorarinsdottir, Thordis L.;Gneiting, Tilmann
在关于预测质量的公开讨论中，注意力通常集中在极端事件的预测性能上。然而，传统的预测评估方法的极端观测子集的限制有意想不到的和不希望的影响，并势必怀疑熟练的预测时，在数据生成过程中的信噪比是低的。结果的条件是不符合既定的预测评估方法的理论假设，从而面临着我们所说的预测者的困境预测。对于概率预报，已提出适当的加权评分规则，作为强调极端事件的预报评估的决策理论上合理的替代方案。使用理论论证，模拟实验和真实的数据研究美国通货膨胀和国内生产总值（GDP）增长的概率预测，我们说明和讨论预测者的困境沿着与潜在的补救措施。
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.