关于胖尾变量的单点预测
On Single Point Forecasts for Fat-Tailed Variables
摘要：
我们讨论了常见的错误和谬误时，使用天真的“证据为基础的”实证主义和点预测的厚尾变量，以及使用天真的一阶科学方法尾部风险管理的不足。我们以COVID-19疫情作为讨论的背景，并作为一个以倍增性质为特征的现象的例子，以及统计特性和相关风险必须产生的缓解政策。在此过程中，我们还回应了Ioannidis等人（2020）提出的观点。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
