Sample Covariance Shrinkage for High Dimensional Dependent Data

Alessio Sancetta

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: 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.

Keywords: Sample Covariance Matrix; Shrinkage; Weak Dependence (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Pages: 25
Date: 2006-05
New Economics Papers: this item is included in nep-ecm
Note: Em
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://files.econ.cam.ac.uk/repec/cam/pdf/cwpe0637.pdf (application/pdf)

Related works:
Journal Article: Sample covariance shrinkage for high dimensional dependent data (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:0637

Access Statistics for this paper

More papers in Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
Bibliographic data for series maintained by Jake Dyer ().