Testing for common autocorrelation in data‐rich environments
AbstractThis 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.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 30 (2011)
Issue (Month): 3 (April)
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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
serial correlation common feature ; high‐dimensional systems ; partial least squares ; reduced‐rank regression ;
Other versions of this item:
- Gianluca Cubadda & Alain Hecq, 2009. "Testing for Common Autocorrelation in Data Rich Environments," CEIS Research Paper 153, Tor Vergata University, CEIS, revised 04 Dec 2009.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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- Dean Croushore & Tom Stark, 1999.
"A real-time data set for macroeconomists,"
99-4, Federal Reserve Bank of Philadelphia.
- 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.
- Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York.
- 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 McConnell & Gabriel Perez Quiros, 2000. "Output fluctuations in the United States: what has changed since the early 1980s?," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
- 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.
- 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.
- 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.
- 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 & Guardabascio, Barbara, 2012.
"A medium-N approach to macroeconomic forecasting,"
Elsevier, vol. 29(4), pages 1099-1105.
- Hecq Alain & Laurent Sébastien & Palm Franz, 2011. "On the Univariate Representation of Multivariate Volatility Models with Common Factors," Research Memorandum 011, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2013.
"A general to specific approach for constructing composite business cycle indicators,"
Elsevier, vol. 33(C), pages 367-374.
- 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.
- Marco Centoni & Gianluca Cubadda, 2011.
"Modelling comovements of economic time series: a selective survey,"
Department of Statistics, University of Bologna, vol. 71(2), pages 267-294.
- 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.
- Hecq Alain & Laurent Sébastien & Palm Franz C., 2012. "On the Univariate Representation of BEKK Models with Common Factors," Research Memorandum 018, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
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