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

Jushan Bai, Xu Han and Yutang Shi

Journal of Econometrics, 2020, vol. 219, issue 1, 66-100

Abstract: 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.

Keywords: Structural changes; High-dimensional factor models; Break point inference (search for similar items in EconPapers)
JEL-codes: C12 C22 C38 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:219:y:2020:i:1:p:66-100

DOI: 10.1016/j.jeconom.2019.08.013

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