In this paper, we propose a method for detecting multiple change-points in the mean of (possibly) 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, each of which is computed on a single panel. For panel data of high dimensions, the detectability of a change-point is influenced by several factors, such as its sparsity across the panels, the magnitude of jumps at the change-point 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 in panel data analysis.
The Sparsified Binary Segmentation and the Double CUSUM Binary Segmentation represent determined efforts in this direction. We investigate under which conditions the two binary segmentation methods attain consistent change-point detection in terms of both the total number and the locations of detected change-points, and conduct a comparative simulation study in which its good performance is demonstrated.