Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/130778
Authors: 
Bailey, Natalia
Pesaran, M. Hashem
Smith, L. Vanessa
Year of Publication: 
2015
Series/Report no.: 
Working Paper, School of Economics and Finance, Queen Mary University of London 764
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. By using the inverse of the normal distribution at a predetermined significance level, it circumvents the challenge of estimating the theoretical constant arising in the rate of convergence of existing thresholding estimators, and hence it is easy to implement and does not require cross-validation. 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 the cross section dimension, N, is larger than the time series dimension, T.
Subjects: 
Sparse correlation matrices
High-dimensional data
Multiple testing
Thresholding
Shrinkage
JEL: 
C13
C58
Document Type: 
Working Paper

Files in This Item:
File
Size
749.44 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.