Detection performance of likelihood ratio test for change points based on bootstrap for AR 1 models

Date

2016-08-26

Author

Yazıcı, Ceyda
Yozgatlıgil, Ceylan
Batmaz, İnci

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he detection of change-points in time series is an important issue especially in economics, finance, meteorology and energy. Change in mean, change in variance or any sudden increase or decrease in the series can cause breakpoints. In AR(1) models, the likelihood ratio test is conducted to test for a single breakpoint. However, if the sample size is small or the location of the breakpoint is close to the end or the beginning of the series, the detection performance becomes worse. In order to increase the correct detection percentage of the likelihood ratio test in these cases, a bootstrap method for dependent data is applied and its performance is investigated when the change is only in the mean under several breakpoint scenarios. The test is applied to simulated data and the results are compared with the results obtained from tests in the literature.

Subject Keywords

Bootstrap, Change points, Simulation, Time series

URI

http://cmstatistics.org/RegistrationsV2/COMPSTAT2016/viewSubmission.php?id=527&token=np263qr78sn50660043or043rr3p63qo
https://hdl.handle.net/11511/78839

Conference Name

The 22nd International Conference on Computational Statistics, ( 23 - 26 Ağustos 2016)

Collections

Department of Statistics, Conference / Seminar

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Citation Formats

C. Yazıcı, C. Yozgatlıgil, and İ. Batmaz, “Detection performance of likelihood ratio test for change points based on bootstrap for AR 1 models,” presented at the The 22nd International Conference on Computational Statistics, ( 23 - 26 Ağustos 2016), Oviedo, İspanya, 2016, Accessed: 00, 2021. [Online]. Available: http://cmstatistics.org/RegistrationsV2/COMPSTAT2016/viewSubmission.php?id=527&token=np263qr78sn50660043or043rr3p63qo.