Change Point Detection in Autoregression Without Variability Estimation

0484133 - ÚI 2018 RIV ES eng C - Conference Paper (international conference)
Peštová, Barbora - Pešta, M.
Change Point Detection in Autoregression Without Variability Estimation.
Proceedings of the International work-conference on Time Series 2017. Granada: Godel Editorial, 2017 - (Valenzuela, O.; Rojas, F.; Pomares, H.; Rojas, I.), s. 674-685. ISBN 978-84-17293-01-7.
[ITISE 2017. International Work-Conference on Time Series. Granada (ES), 18.09.2017-20.09.2017]
R&D Projects: GA ČR(CZ) GBP402/12/G097
Institutional support: RVO:67985807
Keywords : change point * structural change * change in autoregression * hypothesis testing * ratio type statistic * variance estimation free test
OECD category: Pure mathematics

A sequence of time-ordered observations follows an autoregressive model of order one and its parameter is possibly subject to change at most once at some unknown time point. The aim is to test whether such an unknown change has occurred or not. A change point method presented here rely on a ratio type test statistic based on the maxima of cumulative sums. The main advantage of the proposed approach is that the variance of the observations neither has to be known nor estimated. Asymptotic distribution of the test statistic under the no change null hypothesis is derived. Moreover, we prove the consistency of the test under the alternative. The results are illustrated through a simulation study, which demonstrates computational e
Permanent Link: http://hdl.handle.net/11104/0279300