Estimation and Inference of Change Points in High Dimensional Factor Models
71 Pages Posted: 29 Nov 2016 Last revised: 23 Jan 2019
Date Written: November 25, 2018
In this paper, we consider the estimation of break points in high-dimensional factor models where the unobserved factors are estimated by principal component analysis (PCA). The factor loading matrix is assumed to have a structural break at an unknown time. We establish the conditions under which the least squares (LS) estimator is consistent for the break date. Our consistency result holds for both large and smaller breaks. We also find the LS estimator’s asymptotic distribution. Simulation results confirm that the break date can be accurately estimated by the LS even if the breaks are small. In two empirical applications, we implement our method to estimate break points in the U.S. stock market and U.S. macroeconomy, respectively.
Keywords: Structural Changes, High-Dimensional Factor Models, Break Point Inference
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