Testing for Common Autocorrelation in Data Rich Environments

This paper proposes a strategy to detect the presence of common serial correlation in high-dimensional systems. We show by simulations that univariate autocorrelation tests on the factors obtained by partial least squares outperform traditional tests based on canonical correlations

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Year of publication:	2014
Authors:	Cubadda, Gianluca
Other Persons:	Hecq, Alain (contributor)
Publisher:	[2014]: [S.l.] : SSRN

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Extent:	1 Online-Ressource (11 p)
Series:	CEIS Working Paper ; No. 153
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 4, 2009 erstellt
Other identifiers:	10.2139/ssrn.1518419 [DOI]
Classification:	C32 - Time-Series Models
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10013070493