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Open AccessArticle

Change Point Estimation in Panel Data without Boundary Issue

by Barbora Peštová 1,† and Michal Pešta 2,*,†
1
Department of Medical Informatics and Biostatistics, Institute of Computer Science, The Czech Academy of Sciences, Pod Vodárenskou věží 271/2, 18207 Prague 8, Czech Republic
2
Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University, Sokolovská 83, 18675 Prague 8, Czech Republic
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Qihe Tang
Received: 28 August 2016 / Revised: 22 December 2016 / Accepted: 17 January 2017 / Published: 22 January 2017
Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented, as well. View Full-Text
Keywords: change point; estimation; consistency; panel data; short panels; boundary issue; structural change; bootstrap; non-life insurance; change in claim amounts change point; estimation; consistency; panel data; short panels; boundary issue; structural change; bootstrap; non-life insurance; change in claim amounts
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MDPI and ACS Style

Peštová, B.; Pešta, M. Change Point Estimation in Panel Data without Boundary Issue. Risks 2017, 5, 7.

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