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Testing for Common Autocorrelation in Data Rich Environments

Gianluca Cubadda () and Alain Hecq ()

No 153, CEIS Research Paper from Tor Vergata University, CEIS

Abstract: 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.

Keywords: Serial correlation common feature; high-dimensional systems; partial least squares. JEL code: C32 (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2009-12-04, Revised 2009-12-04
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ftp://www.ceistorvergata.it/repec/rpaper/RP153.pdf Main text (application/pdf)

Related works:
Journal Article: Testing for common autocorrelation in data‐rich environments (2011) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:rtv:ceisrp:153

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