Estimation and Inference of Change Points in High Dimensional Factor Models

71 Pages Posted: 29 Nov 2016 Last revised: 23 Jan 2019

See all articles by Jushan Bai

Jushan Bai

Columbia University

Xu Han

City University of Hong Kong (CityUHK) - Department of Economics & Finance

Yutang Shi

Texas A&M University - Department of Economics

Date Written: November 25, 2018

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 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

Suggested Citation

Bai, Jushan and Han, Xu and Shi, Yutang, Estimation and Inference of Change Points in High Dimensional Factor Models (November 25, 2018). Available at SSRN: https://ssrn.com/abstract=2875193 or http://dx.doi.org/10.2139/ssrn.2875193

Jushan Bai

Columbia University ( email )

3022 Broadway
New York, NY 10027
United States

Xu Han (Contact Author)

City University of Hong Kong (CityUHK) - Department of Economics & Finance ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Yutang Shi

Texas A&M University - Department of Economics ( email )

5201 University Blvd.
College Station, TX 77843-4228
United States

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