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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