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Testing for common autocorrelation in data‐rich environments
Author(s) -
Cubadda Gianluca,
Hecq Alain
Publication year - 2011
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1186
Subject(s) - autocorrelation , univariate , partial least squares regression , partial autocorrelation function , econometrics , monte carlo method , statistics , canonical correlation , computer science , generalized least squares , mathematics , algorithm , data mining , time series , autoregressive integrated moving average , multivariate statistics , estimator
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.