论文标题
模型不确定性下的强大数据驱动决策
Robust Data-Driven Decisions Under Model Uncertainty
论文作者
论文摘要
当样本数据由一个未知的独立但可能是非相同的分布的序列来控制时，数据生成过程（DGP）通常无法从数据中完美地识别。为了做出这种不确定性面临的决策，本文通过研究如何最佳地使用数据来稳健地改善决策，从而提出了一种新颖的方法。也就是说，无论哪种DGP控制不确定性，都可以做出更好的决定，而不是使用数据。我表明，例如，最大可能性和贝叶斯更新无法实现此目标。为了解决问题，我制定了新的更新规则，这些规则几乎肯定地渐近地做出了强有力的更好决策，或者在有限的样本中具有预先指定的概率。特别是，在可能的DGP都是独立且分布相同的情况下，标准统计程序的简单扩展所给出的，它们易于实现。最后，我表明，新的更新规则还会在现有的经济模型（例如歧义性的资产定价）中得出更直观的结论。
When sample data are governed by an unknown sequence of independent but possibly non-identical distributions, the data-generating process (DGP) in general cannot be perfectly identified from the data. For making decisions facing such uncertainty, this paper presents a novel approach by studying how the data can best be used to robustly improve decisions. That is, no matter which DGP governs the uncertainty, one can make a better decision than without using the data. I show that common inference methods, e.g., maximum likelihood and Bayesian updating cannot achieve this goal. To address, I develop new updating rules that lead to robustly better decisions either asymptotically almost surely or in finite sample with a pre-specified probability. Especially, they are easy to implement as are given by simple extensions of the standard statistical procedures in the case where the possible DGPs are all independent and identically distributed. Finally, I show that the new updating rules also lead to more intuitive conclusions in existing economic models such as asset pricing under ambiguity.
