Bailey, Natalia, Pesaran, M. Hashem and Smith, Lynette Vanessa orcid.org/0000-0003-0489-047X (2019) A multiple testing approach to the regularisation of large sample correlation matrices. Journal of Econometrics. pp. 507-534. ISSN 0304-4076
Abstract
This paper proposes a regularisation method for the estimation of large covariance matrices that uses insights from the multiple testing (MT) literature. The approach tests the statistical significance of individual pair-wise correlations and sets to zero those elements that are not statistically significant, taking account of the multiple testing nature of the problem. The effective p-values of the tests are set as a decreasing function of N (the cross section dimension), the rate of which is governed by the nature of dependence of the underlying observations, and the relative expansion rates of N and T (the time dimension). In this respect, the method specifies the appropriate thresholding parameter to be used under Gaussian and non-Gaussian settings. The MT estimator of the sample correlation matrix is shown to be consistent in the spectral and Frobenius norms, and in terms of support recovery, so long as the true covariance matrix is sparse. The performance of the proposed MT estimator is compared to a number of other estimators in the literature using Monte Carlo experiments. It is shown that the MT estimator performs well and tends to outperform the other estimators, particularly when N is larger than T.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier B.V. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | High-dimensional data,Multiple testing,Non-Gaussian observations,Shrinkage,Sparsity,Thresholding |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Social Sciences (York) > Economics and Related Studies (York) |
Depositing User: | Pure (York) |
Date Deposited: | 01 Nov 2018 12:10 |
Last Modified: | 26 Nov 2024 00:40 |
Published Version: | https://doi.org/10.1016/j.jeconom.2018.10.006 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.jeconom.2018.10.006 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:138089 |