Testing for common autocorrelation in data‐rich environments
This paper proposes a strategy to detect the presence of common serial cor- relation in large‐dimensional systems. We show that partial least squares can be used to consistently recover the common autocorrelation space. Moreover, a Monte Carlo study reveals that univariate autocorrelation tests on the factors obtained by partial least squares outperform traditional tests based on canonical correlation analysis. Some empirical applications are presented to illustrate concepts and methods. Copyright (C) 2010 John Wiley & Sons, Ltd.
Volume (Year): 30 (2011)
Issue (Month): 3 (April)
|Contact details of provider:|| Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Margaret M. McConnell & Gabriel Perez-Quiros, 2000.
"Output fluctuations in the United States: what has changed since the early 1980s?,"
Federal Reserve Bank of San Francisco, issue Mar.
- 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.
- 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.
- Margaret M. McConnell & Gabriel Perez Quiros, 1997. "Output fluctuations in the United States: what has changed since the early 1980s?," Research Paper 9735, Federal Reserve Bank of New York.
- Jan J.J. Groen & George Kapetanios, 2008.
"Revisiting Useful Approaches to Data-Rich Macroeconomic Forecasting,"
624, Queen Mary University of London, School of Economics and Finance.
- Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
- Groen, Jan J. J. & Kapetanios, George, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York, revised 01 Oct 2015.
- Dean Croushore & Tom Stark, 1999.
"A real-time data set for macroeconomists,"
99-4, Federal Reserve Bank of Philadelphia.
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:jof:jforec:v:30:y:2011:i:3:p:325-335. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing)or (Christopher F. Baum)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.