IPAD：基于Knockoffs推理的稳定可解释预测
IPAD: Stable Interpretable Forecasting with Knockoffs Inference
摘要：
可解释性和稳定性是经济和金融领域中许多当代大数据应用所期望的两个重要特征。虽然许多现有的预测方法在某种程度上都喜欢前者，但在计量经济学环境中，后者在控制错误发现的特征的比例方面仍然很不发达，这可以大大提高可解释性。为此，在本文中，我们利用了最近在Candès，Fan，Janson和Lv（2018）中引入的模型X仿制品的一般框架，这对于可再现的大规模推理来说是非常规的，因为该框架完全不使用p值进行显著性检验，并提出了一种新的交织概率因素解耦（iPad）方法，用于稳定的可解释预测，并在高水平上进行仿冒推断。维度模型。该方法的秘诀是通过假设一个在经济学和金融学中广泛使用的协变量关联结构的潜在因素模型来构造敲除变量。我们的方法和工作是不同于现有的文献中，我们估计的协变量分布的数据，而不是假设它是已知的构建敲除变量时，我们的程序不需要任何样本分裂，我们提供了理论上的理由渐近错误发现率控制，和功率分析的理论也成立。仿真实例和实际数据分析进一步表明，与一些常用的预测方法相比，该方法具有良好的有限样本性能，并具有良好的可解释性和稳定性。
英文摘要：
Interpretability and stability are two important features that are desired in many contemporary big data applications arising in economics and finance. While the former is enjoyed to some extent by many existing forecasting approaches, the latter in the sense of controlling the fraction of wrongly discovered features which can enhance greatly the interpretability is still largely underdeveloped in the econometric settings. To this end, in this paper we exploit the general framework of model-X knockoffs introduced recently in Candès, Fan, Janson and Lv (2018), which is nonconventional for reproducible large-scale inference in that the framework is completely free of the use of p-values for significance testing, and suggest a new method of intertwined probabilistic factors decoupling (IPAD) for stable interpretable forecasting with knockoffs inference in high-dimensional models. The recipe of the method is constructing the knockoff variables by assuming a latent factor model that is exploited widely in economics and finance for the association structure of covariates. Our method and work are distinct from the existing literature in that we estimate the covariate distribution from data instead of assuming that it is known when constructing the knockoff variables, our procedure does not require any sample splitting, we provide theoretical justifications on the asymptotic false discovery rate control, and the theory for the power analysis is also established. Several simulation examples and the real data analysis further demonstrate that the newly suggested method has appealing finite-sample performance with desired interpretability and stability compared to some popularly used forecasting methods.
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