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.
|Date of creation:||04 Dec 2009|
|Date of revision:||04 Dec 2009|
|Contact details of provider:|| Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma|
Web page: http://www.ceistorvergata.it
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|Order Information:|| Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma|
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