Testing for Common Autocorrelation in Data Rich Environments

A.W. Hecq, G. Cubadda*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Pages (from-to)325-335
JournalJournal of Forecasting
Issue number3
Publication statusPublished - 1 Jan 2011

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