Tests for principal eigenvalues and eigenvectors

Fuente: arXiv
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Hauptverfasser: Fan, Jianqing, Li, Yingying, Xia, Ningning, Zheng, Xinghua
Format: Preprint
Veröffentlicht: 2024
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author Fan, Jianqing
Li, Yingying
Xia, Ningning
Zheng, Xinghua
author_facet Fan, Jianqing
Li, Yingying
Xia, Ningning
Zheng, Xinghua
contents We establish central limit theorems for principal eigenvalues and eigenvectors under a large factor model setting, and develop two-sample tests of both principal eigenvalues and principal eigenvectors. One important application is to detect structural breaks in large factor models. Compared with existing methods for detecting structural breaks, our tests provide unique insights into the source of structural breaks because they can distinguish between individual principal eigenvalues and/or eigenvectors. We demonstrate the application by comparing the principal eigenvalues and principal eigenvectors of S\&P500 Index constituents' daily returns over different years.
format Preprint
id arxiv_https___arxiv_org_abs_2405_06939
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Tests for principal eigenvalues and eigenvectors
Fan, Jianqing
Li, Yingying
Xia, Ningning
Zheng, Xinghua
Statistics Theory
We establish central limit theorems for principal eigenvalues and eigenvectors under a large factor model setting, and develop two-sample tests of both principal eigenvalues and principal eigenvectors. One important application is to detect structural breaks in large factor models. Compared with existing methods for detecting structural breaks, our tests provide unique insights into the source of structural breaks because they can distinguish between individual principal eigenvalues and/or eigenvectors. We demonstrate the application by comparing the principal eigenvalues and principal eigenvectors of S\&P500 Index constituents' daily returns over different years.
title Tests for principal eigenvalues and eigenvectors
topic Statistics Theory
url https://arxiv.org/abs/2405.06939