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Testing for common autocorrelation in data‐rich environments

Gianluca Cubadda () and Alain Hecq ()

Journal of Forecasting, 2011, vol. 30, issue 3, pages 325-335

Abstract: 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.

Keywords: serial correlation common feature; high‐dimensional systems; partial least squares; reduced‐rank regression (search for similar items in EconPapers)
Date: 2011
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http://hdl.handle.net/10.1002/for.1186

Related works:
Working Paper: Testing for Common Autocorrelation in Data Rich Environments (2009) Downloads
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Persistent link: http://EconPapers.repec.org/RePEc:jof:jforec:v:30:y:2011:i:3:p:325-335

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