In this talk, 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 panel, by searching for change-points from the aggregation of multiple series of CUSUM statistics, each of which is computed on a single series of the panel data.
The double CUSUM statistic is proposed as a determined effort for achieving consistency in detecting and locating (possibly multiple) change-points in the panel data. Its efficiency in change-point detection is investigated in terms of the cross-sectional size of the change, the unbalancedness of change-point location and within-series and cross-sectional correlations in the panel data. Also, a comparative simulation study is conducted where the proposed method is applied to a range of change-point scenarios along with the state-of-the-art.