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2016 Change-point detection in panel data via double CUSUM statistic
Haeran Cho
Electron. J. Statist. 10(2): 2000-2038 (2016). DOI: 10.1214/16-EJS1155

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.

Citation

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Haeran Cho. "Change-point detection in panel data via double CUSUM statistic." Electron. J. Statist. 10 (2) 2000 - 2038, 2016. https://doi.org/10.1214/16-EJS1155

Information

Received: 1 November 2015; Published: 2016
First available in Project Euclid: 18 July 2016

zbMATH: 06624508
MathSciNet: MR3522667
Digital Object Identifier: 10.1214/16-EJS1155

Rights: Copyright © 2016 The Institute of Mathematical Statistics and the Bernoulli Society

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Vol.10 • No. 2 • 2016
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