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Testing for Common Autocorrelation in Data Rich Environments

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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.

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File URL: ftp://www.ceistorvergata.it/repec/rpaper/RP153.pdf
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Bibliographic Info

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

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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
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Web page: http://www.ceistorvergata.it
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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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Web: http://www.ceistorvergata.it

Related research

Keywords: Serial correlation common feature; high-dimensional systems; partial least squares. JEL code: C32;

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References

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  1. 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.
  2. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2009. "Studying co-movements in large multivariate data prior to multivariate modelling," Journal of Econometrics, Elsevier, vol. 148(1), pages 25-35, January.
  3. repec:dgr:umamet:2007032 is not listed on IDEAS
  4. Dean Croushore & Tom Stark, 1999. "A real-time data set for macroeconomists," Working Papers 99-4, Federal Reserve Bank of Philadelphia.
  5. Gabriel Perez-Quiros & Margaret M. McConnell, 2000. "Output Fluctuations in the United States: What Has Changed since the Early 1980's?," American Economic Review, American Economic Association, vol. 90(5), pages 1464-1476, December.
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Cited by:
  1. Gianluca Cubadda & Barbara Guardabascio & Alain Hecq, 2012. "A General to Specific Approach for Constructing Composite Business Cycle Indicators," CEIS Research Paper 224, Tor Vergata University, CEIS, revised 27 Feb 2012.
  2. Marco Centoni & Gianluca Cubadda, 2011. "Modelling Comovements of Economic Time Series: A Selective Survey," CEIS Research Paper 215, Tor Vergata University, CEIS, revised 26 Oct 2011.
  3. repec:dgr:umamet:2012018 is not listed on IDEAS
  4. Cubadda, Gianluca & Guardabascio, Barbara, 2012. "A medium-N approach to macroeconomic forecasting," Economic Modelling, Elsevier, vol. 29(4), pages 1099-1105.

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