高维因子模型中群体特异性异质性的检验

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Antoine Djogbenou, Razvan Sufana
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引用次数: 0

摘要

标准的高维因素模型假设,可以使用影响所有变量的少量潜在因素对一大组变量中的协同运动进行建模。在经济学和金融学的许多相关应用中,一些已知变量组特有的异质共动自然会出现,并反映出这些组中不同的周期性运动。本文开发了两种新的统计测试,可用于调查是否有证据支持数据中的特定群体异质性。本文还提出并证明了置换方法逼近两个检验统计量的渐近分布的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tests for group-specific heterogeneity in high-dimensional factor models

Standard high-dimensional factor models assume that the comovements in a large set of variables could be modeled using a small number of latent factors that affect all variables. In many relevant applications in economics and finance, heterogeneous comovements specific to some known groups of variables naturally arise, and reflect distinct cyclical movements within those groups. This paper develops two new statistical tests that can be used to investigate whether there is evidence supporting group-specific heterogeneity in the data. The paper also proposes and proves the validity of a permutation approach for approximating the asymptotic distributions of the two test statistics.

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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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