A0220
Title: Change point inference in high-dimensional regression models under temporal dependence
Authors: Daren Wang - University of Notre Dame (United States) [presenting]
Abstract: Detecting when the underlying distribution changes for the observed time series is a fundamental problem in a broad spectrum of applications. The limiting distributions of change point estimators in the high-dimensional linear regression time series context are considered. At unknown time points, called change points, the regression coefficients change, with the jump sizes measured in l2-norm. Limiting distributions of the change point estimators will be discussed in the regimes where the minimal jump size vanishes and remains constant. The covariate and noise sequences are allowed to be temporally dependent in the functional dependence framework, which is the first time seen in the change point inference literature. A block-type long-run variance estimator is shown to be consistent under the functional dependence framework, which facilitates the practical implementation of the derived limiting distributions. Extensive numerical results are provided to support the theoretical findings.
