结构变化检测中FDR控制的广义模拟程序

IF 9.9 3区 经济学 Q1 ECONOMICS Journal of Econometrics Pub Date : 2024-02-01 DOI:10.1016/j.jeconom.2022.07.008
Jingyuan Liu , Ao Sun , Yuan Ke
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引用次数: 0

摘要

控制误发现率(FDR)对于变量选择、多重测试以及其他信号检测问题至关重要。在文献中,当然不乏选择单个特征时的 FDR 控制策略,但用于结构变化检测的相关著作却很有限,如片断常数系数的轮廓分析和多数据源的整合分析。在本文中,我们提出了一种在此类问题设置下进行 FDR 控制的广义敲除程序(GKnockoff)。我们证明,GKnockoff 具有成对交换性,能够在有限样本量下控制精确的 FDR。我们进一步探索了高维条件下的 GKnockoff,首先引入了一种新的筛选方法来过滤高维的潜在结构变化。我们采用数据分割技术,首先通过筛选降低维度,然后在精选集上进行 GKnockoff。此外,我们还系统地研究了所提方法的威力。与其他方法的数值比较表明,GKnockoff 在 FDR 控制和功率方面都表现出色。我们还将提出的方法用于分析宏观经济数据集,以检测经济发展对第二产业的驱动效应变化。
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A generalized knockoff procedure for FDR control in structural change detection

Controlling false discovery rate (FDR) is crucial for variable selection, multiple testing, among other signal detection problems. In literature, there is certainly no shortage of FDR control strategies when selecting individual features, but the relevant works for structural change detection, such as profile analysis for piecewise constant coefficients and integration analysis with multiple data sources, are limited. In this paper, we propose a generalized knockoff procedure (GKnockoff) for FDR control under such problem settings. We prove that the GKnockoff possesses pairwise exchangeability, and is capable of controlling the exact FDR under finite sample sizes. We further explore GKnockoff under high dimensionality, by first introducing a new screening method to filter the high-dimensional potential structural changes. We adopt a data splitting technique to first reduce the dimensionality via screening and then conduct GKnockoff on the refined selection set. Furthermore, the powers of proposed methods are systematically studied. Numerical comparisons with other methods show the superior performance of GKnockoff, in terms of both FDR control and power. We also implement the proposed methods to analyze a macroeconomic dataset for detecting changes of driven effects of economic development on the secondary industry.

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来源期刊
Journal of Econometrics
Journal of Econometrics 社会科学-数学跨学科应用
CiteScore
8.60
自引率
1.60%
发文量
220
审稿时长
3-8 weeks
期刊介绍: The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.
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