Sample covariance shrinkage for high dimensional dependent data

For high dimensional data sets the sample covariance matrix is usually unbiased but noisy if the sample is not large enough. Shrinking the sample covariance towards a constrained, low dimensional estimator can be used to mitigate the sample variability. By doing so, we introduce bias, but reduce variance. In this paper, we give details on feasible optimal shrinkage allowing for time series dependent observations.

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Year of publication:	2008
Authors:	Sancetta, Alessio
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 99.2008, 5, p. 949-967
Publisher:	Elsevier
Keywords:	Sample covariance matrix Shrinkage Weak dependence

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005093918