Testing for common autocorrelation in data‐rich environments |
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Authors: | Gianluca Cubadda Alain Hecq |
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Affiliation: | 1. Dipartimento SEFEMEQ, Università di Roma ‘Tor Vergata’, Rome, Italy;2. Maastricht University, The Netherlands |
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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 © 2010 John Wiley & Sons, Ltd. |
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Keywords: | serial correlation common feature high‐dimensional systems partial least squares reduced‐rank regression |
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