Publication: Detecting big structural breaks in large factor models
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2011-12
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Abstract
Time invariance of factor loadings is a standard assumption in the analysis of large
factor models. Yet, this assumption may be restrictive unless parameter shifts are
mild (i.e., local to zero). In this paper we develop a new testing procedure to
detect big breaks in these loadings at either known or unknown dates. It relies upon
testing for parameter breaks in a regression of the first of the r¯ factors estimated by
PCA on the remaining r¯ − 1 factors, where r¯ is chosen according to Bai and Ng’s
(2002) information criteria. The test fares well in terms of power relative to other
recently proposed tests on this issue, and can be easily implemented to avoid
forecasting failures in standard factor-augmented (FAR, FAVAR) models where the
number of factors is a priori imposed on the basis of theoretical considerations.
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Structural break, Large factor model, Loadings, Principal components