Quantile factor models (QFM) represent a new class of factor models for high-dimensional panel data. Unlike approximate factor models (AFM), which only extract mean factors, QFM also allow unobserved factors to shift other relevant parts of the distributions of observables. We propose a quantile...

Constant factor loadings is a standard assumption in the analysis of large dimensional factor models. Yet, this assumption may be restrictive unless parameter shifts are mild. In this paper we develop a new testing procedure to detect big breaks in factor loadings at either known or unknown...

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...