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:	2006-05
Authors:	Sancetta, A.
Institutions:	Faculty of Economics, University of Cambridge
Subject:	Sample Covariance Matrix \| Shrinkage \| Weak Dependence

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Extent:	application/pdf
Series:	Cambridge Working Papers in Economics.
Type of publication:	Book / Working Paper
Language:	English
Notes:	Em 2 pages long
Classification:	C13 - Estimation ; C14 - Semiparametric and Nonparametric Methods
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005650534