Inference of Break-Points in High-Dimensional Time Series

73 Pages Posted: 29 Sep 2020

See all articles by Likai Chen

Likai Chen

University of Chicago - Department of Statistics

Weining Wang

affiliation not provided to SSRN; University of York

Weibiao Wu

University of Chicago - Department of Statistics

Date Written: April 25, 2019

Abstract

We consider a new procedure for detecting structural breaks in mean for high-dimensional time series. We target breaks happening at unknown time points and locations. In particular, at a fixed time point our method is concerned with either the biggest break in one location or aggregating simultaneous breaks over multiple locations. We allow for both big or small sized breaks, so that we can 1), stamp the dates and the locations of the breaks, 2), estimate the break sizes and 3), make inference on the break sizes as well as the break dates. Our theoretical setup incorporates both temporal and cross-sectional dependence, and is suitable for heavy-tailed innovations. We derive the asymptotic distribution for the sizes of the breaks by extending the existing powerful theory on local linear kernel estimation and high dimensional Gaussian approximation to allow for trend stationary time series with jumps. A robust long-run covariance matrix estimation is proposed, which can be of independent interest. An application on detecting structural changes of the US unemployment rate is considered to illustrate the usefulness of our method.

Keywords: high-dimensional time series, multiple change-points, Gaussian approximation, nonparametric estimation, heavy tailed, long-run covariance matrix

JEL Classification: C00

Suggested Citation

Chen, Likai and Wang, Weining and Wang, Weining and Wu, Weibiao, Inference of Break-Points in High-Dimensional Time Series (April 25, 2019). Available at SSRN: https://ssrn.com/abstract=3378221 or http://dx.doi.org/10.2139/ssrn.3378221

Likai Chen

University of Chicago - Department of Statistics ( email )

Eckhart Hall Room 108
5734 S. University Avenue
Chicago, IL 60637
United States

Weining Wang (Contact Author)

University of York ( email )

Department of Economics and Related Studies Univer
York, YO10 5DD
United Kingdom

affiliation not provided to SSRN

Weibiao Wu

University of Chicago - Department of Statistics ( email )

Chicago, IL 60637

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