Change-point detection in panel data via double CUSUM statistic

Haeran Cho*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

71 Citations (Scopus)
660 Downloads (Pure)


In this paper, we consider the problem of (multiple) changepoint detection in panel data. We propose the double CUSUM statistic which utilises the cross-sectional change-point structure by examining the cumulative sums of ordered CUSUMs at each point. The efficiency of the proposed change-point test is studied, which is reflected on the rate at which the cross-sectional size of a change is permitted to converge to zero while it is still detectable. Also, the consistency of the proposed changepoint detection procedure based on the binary segmentation algorithm, is established in terms of both the total number and locations (in time) of the estimated change-points. Motivated by the representation properties of the Generalised Dynamic Factor Model, we propose a bootstrap procedure for test criterion selection, which accounts for both cross-sectional and within-series correlations in high-dimensional data. The empirical performance of the double CUSUM statistics, equipped with the proposed bootstrap scheme, is investigated in a comparative simulation study with the state-of-the-art. As an application, we analyse the log returns of S&P 100 component stock prices over a period of one year.
Original languageEnglish
Pages (from-to)2000-2038
Number of pages39
JournalElectronic Journal of Statistics
Issue number2
Early online date18 Jul 2016
Publication statusPublished - Jul 2016


  • Binary segmentation
  • Change-point analysis
  • CUSUM statistics
  • High-dimensional data analysis


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