Testing structural changes in panel data with small fixed panel size and bootstrap

Springer Proceedings in Mathematics &Amp; Statistics

2018

Self Cite

The main goal is to develop and, consequently, compare stochastic methods for detection whether a structural change in panel data occurred at some unknown time or not. Panel data of our interest consist of a moderate or relatively large number of panels, while the panels contain a small number of observations. Testing procedures to detect a possible common change in means of the panels are established. Ratio and non-ratio type test statistics are considered. Their asymptotic distributions under the no change null hypothesis are derived. Moreover, we prove the consistency of the tests under the alternative. The main advantage of the ratio type statistics compared to the non-ratio ones is that the variance of the observations neither has to be known nor estimated. A simulation study reveals that the proposed ratio statistic outperforms the non-ratio one by keeping the significance level under the null, mainly when stronger dependence within the panel is taken into account. However, the non-ratio statistic rejects the null in the simulations more often than it should, which yields higher power compared to the ratio statistic.

“…elaborated in Peštová and Pešta (2015). It will be demonstrated by simulations that R N pT q keeps the theoretical significance level, however, C N pT q does not.…”

Section: Test Statistic and Asymptotic Resultsmentioning

confidence: 90%

Change Point in Panel Data with Small Fixed Panel Size: Ratio and Non-ratio Test Statistics

Springer Proceedings in Mathematics &Amp; Statistics

2018

Self Cite

“…Testing procedures for the change in panel means with T fixed, which can be relatively small or moderate, were considered in [2]. The change point estimation in panel data for fixed, as well as for unbounded T was studied by [3].…”

Section: Current State Of the Artmentioning

confidence: 99%

“…A wide range of literature has been published on bootstrapping in the change point problem, e.g., [2,12,13] …”

Section: Bootstrappingmentioning

confidence: 99%

Change Point Estimation in Panel Data without Boundary Issue

2017

Risks

Self Cite

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.

Section: Current State Of the Artmentioning

confidence: 99%

Change Point Estimation in Panel Data without Boundary Issue

2016

Preprint

Self Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: 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. Terms of use: Documents in