论文标题
在单点的脂肪尾变量预测上
On Single Point Forecasts for Fat-Tailed Variables
论文作者
论文摘要
我们讨论使用天真的“基于证据”的经验主义和脂肪尾变量的点预测，以及使用幼稚的一阶科学方法来进行尾巴风险管理的不足。我们使用Covid-19大流行作为讨论的背景，并作为以乘法性质为特征的现象的一个例子，以及统计特性和相关风险必须导致的减轻策略。通过这样做，我们还回应了Ioannidis等人提出的观点。 （2020）。
We discuss common errors and fallacies when using naive "evidence based" empiricism and point forecasts for fat-tailed variables, as well as the insufficiency of using naive first-order scientific methods for tail risk management. We use the COVID-19 pandemic as the background for the discussion and as an example of a phenomenon characterized by a multiplicative nature, and what mitigating policies must result from the statistical properties and associated risks. In doing so, we also respond to the points raised by Ioannidis et al. (2020).
