Change-point detection in panel data via double CUSUM statistic

Abstract

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.

References in corpus (5)

• Wild binary segmentation for multiple change-point detection
• Change-point detection in panel data via double CUSUM statistic
• TFT-bootstrap: Resampling time series in the frequency domain to obtain replicates in the time domain
• Multiscale and multilevel technique for consistent segmentation of nonstationary time series
• Covariance matrix estimation and linear process bootstrap for multivariate time series of possibly increasing dimension

Cited by in corpus (13)

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• A review on minimax rates in change point detection and localisation
• Change Point Estimation in Panel Data with Temporal and Cross-sectional Dependence
• A test for second-order stationarity of time series based on unsystematic sub-samples
• Change-point detection using spectral PCA for multivariate time series
• Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference
• Adaptive Inference for Change Points in High-Dimensional Data
• Scalable Bayesian change point detection with spike and slab priors
• A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence
• Estimation of high-dimensional change-points under a group sparsity structure
• Segmentation of high dimensional means over multi-dimensional change points and connections to regression trees
• Inference on the change point in high dimensional time series models via plug in least squares