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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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File URL: ftp://www.ceistorvergata.it/repec/rpaper/RP153.pdf
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Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 153.

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Length: 11 pages
Date of creation: 04 Dec 2009
Date of revision: 04 Dec 2009
Handle: RePEc:rtv:ceisrp:153
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
Phone: +390672595601
Fax: +39062020687
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
Web: http://www.ceistorvergata.it Email:


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  1. Gianluca Cubadda & Alain Hecq & Franz C. Palm, 2008. "Studying Co-Movements in Large Multivariate Data Prior to Multivariate Modelling," CEIS Research Paper 125, Tor Vergata University, CEIS, revised 14 Jul 2008.
  2. Jan J.J. Groen & George Kapetanios, 2008. "Revisiting Useful Approaches to Data-Rich Macroeconomic Forecasting," Working Papers 624, Queen Mary University of London, School of Economics and Finance.
  3. Margaret M. McConnell & Gabriel Perez Quiros, 1998. "Output fluctuations in the United States: what has changed since the early 1980s?," Staff Reports 41, Federal Reserve Bank of New York.
  4. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
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