Detecting Big Structural Breaks in Large Factor Models

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 one of the factors estimated by Principal Components analysis on the remaining estimated factors, where the number of factors 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.