Change-point detection in panel data via double CUSUM statistic

By Haeran Cho. 2016

159 citations recorded by the source corpus.

In this paper, we consider the problem of (multiple) change-point 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 change-point 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.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ed6b400ecbd67ff46a28b657d28a48e18962268d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Yi Yu
2. Mengjia Yu
3. Alessandro Rinaldo
4. Xiaohui Chen
5. Nazar Buzun
6. Valeriy Avanesov
7. Daren Wang

In Statistics and Probability

1. Mengjia Yu
2. Yi Yu
3. Alessandro Rinaldo
4. Xiaohui Chen
5. Nazar Buzun
6. Valeriy Avanesov
7. Daren Wang
8. Kengo Kato

Citations in the corpus

• Change-point detection in high-dimensional covariance structure (2016, background): “Clearly, it seems attractive to choose the threshold in a solely datadriven way as it is suggested in the recent paper [11], but a careful bootstrap validation is still an open question.”
• Simultaneous multiple change-point and factor analysis for high-dimensional time series (2016, unknown): “CUSUM series and aggregate the high-dimensional CUSUM series via the Double CUSUM statistic proposed by Cho (2016), which achieves this through point-wise, data-driven partitioning of the panel data.”
• Finite sample change point inference and identification for high‐dimensional mean vectors (2017, methodology): “For comparison with Cho and Fryzlewicz (2015), Cho (2016), Jirak (2015), we refer to Section 5.3 of Wang and Samworth (2018) for a comprehensive simulation study.”
• Univariate mean change point detection: Penalization, CUSUM and optimality (2018, background): “Change point detection problems in high-dimensional means (e.g. Cho and Fryzlewicz, 2015; Cho, 2015; Aston and Kirch, 2014; Jirak, 2015; Wang and Samworth, 2018), in covariance structures (e.g. Aue et al., 2009; Avanesov and Buzun, 2016; Wang et al., 2017), in dynamic networks (e.g. Gibberd and…”