Detecting Multiple Change-Points in Panel Data via Double CUSUM Binary Segmentation

• Activity Number: 161
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: IMS
• Author(s): Haeran Cho*
• Companies: University of Bristol
• Keywords: CUSUM statistics ; change-point detection ; panel data analysis ; binary segmentation

Abstract

We propose a method for detecting multiple change-points in the mean of high-dimensional panel data. CUSUM statistics have been widely adopted for change-point detection in both univariate and multivariate data. For the latter, it is of particular interest to exploit the cross-sectional structure and achieve simultaneous change-point detection across the panels, by searching for change-points from the aggregation of multiple series of CUSUM statistics. For panel data of high dimensions, the detectability of a change-point is influenced by several factors, e.g., its sparsity across the panels, the size of changes and the unbalancedness of its location, and having a method that handles a wide range of change-point configurations without any prior knowledge is vital. The double CUSUM statistic is a determined effort in this direction, where the key ingredient is data-driven, point-wise partitioning of the panels into those contributing to change-point detection and those which do not. We show that the double CUSUM statistic, jointly with a binary segmentation algorithm, attains consistent change-point detection and conduct a comparative simulation study in which its good performance i