通过相互信息测试无条件和有条件的独立性

IF 9.9 3区 经济学 Q1 ECONOMICS Journal of Econometrics Pub Date : 2024-03-01 DOI:10.1016/j.jeconom.2022.07.011
Chunrong Ai , Li-Hsien Sun , Zheng Zhang , Liping Zhu
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引用次数: 0

摘要

计量经济学和统计学文献对独立性的检验越来越重视。人们提出了许多检验方法,其中大多数方法对所有偏离独立性的情况都是不一致的。这些检验虽然一致,但很少有检验会严重丧失局部能力。本研究提出了一种用于检验独立性的互信息检验。所提出的检验方法简单易行,虽然会有轻微的局部能力损失,但对所有偏离独立性的情况都是一致的。关键的驱动因素是我们直接估算密度比。在独立状态下,该值是恒定的。这与通过估算联合密度函数和边际密度函数来形成密度比的相关研究截然不同。一项小规模的模拟研究表明,在各种依赖结构下,所提出的检验方法优于现有的替代方法。
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Testing unconditional and conditional independence via mutual information

Testing independence has garnered increasing attention in the econometric and statistical literature. Many tests have been proposed, most of which are inconsistent against all departures from independence. Few of those tests, though consistent, suffer a significant loss of local power. This study proposes a mutual information test for testing independence. The proposed test is simple to implement and, with a slight loss of local power, is consistent against all departures from independence. The key driving factor is that we estimate the density ratio directly. This value is constant in a state of independence. This is in contrast with related studies that estimate the joint and marginal density functions to form the density ratio. A small-scale simulation study indicates that the proposed test outperforms the existing alternatives in various dependence structures.

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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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