Estimation and inference of change points in high-dimensional factor models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)66-100
Journal / PublicationJournal of Econometrics
Issue number1
Online published4 May 2020
Publication statusPublished - Nov 2020


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 small 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 magnitudes of breaks are small. In two empirical applications, we implement the method to estimate break points in the U.S. stock market and U.S. macroeconomy, respectively.

Research Area(s)

  • Break point inference, High-dimensional factor models, Structural changes