Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Models.

ABSTRACT: Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to assume piecewise stationarity, where the model can change at potentially many change points. We propose a three-stage procedure for simultaneous estimation of change points and parameters of high-dimensional piecewise vector autoregressive (VAR) models. In the first step, we reformulate the change point detection problem as a high-dimensional variable selection one, and solve it using a penalized least square estimator with a total variation penalty. We show that the penalized estimation method over-estimates the number of change points, and propose a selection criterion to identify the change points. In the last step of our procedure, we estimate the VAR parameters in each of the segm

SUBMITTER: Safikhani A 

PROVIDER: S-EPMC10874880 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature