A0588
Title: Estimation and inference of change points in functional regression time series
Authors: Haotian Xu - Auburn University (United States) [presenting]
Abstract: The purpose is to study the estimation and inference of change points under a functional linear regression model with changes in the slope function. A novel functional regression binary segmentation (FRBS) algorithm is presented, which is computationally efficient and achieves consistency in multiple change point detection. This algorithm utilizes the predictive power of piece-wise constant functional linear regression models in the reproducing kernel Hilbert space framework. A refinement step is further proposed that improves the localization rate of the initial estimator output by FRBS, and asymptotic distributions of the refined estimators for two different regimes are derived, determined by the magnitude of a change. To facilitate the construction of confidence intervals for underlying change points based on the limiting distribution, a consistent block-type long-run variance estimator is proposed. The theoretical investigation accommodates temporal dependence and heavy tails in both the functional covariates and the measurement errors. Empirical performance of the method is demonstrated through extensive simulation studies and applications to financial and economic datasets.
