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dc.contributor.authorYazgan, M. Ege
dc.contributor.authorOzkan, Harun
dc.date.accessioned2019-06-27T08:02:24Z
dc.date.available2019-06-27T08:02:24Z
dc.date.issued2015
dc.identifier.issn1544-6123
dc.identifier.issn1544-6131
dc.identifier.urihttps://hdl.handle.net/20.500.12469/613
dc.identifier.urihttps://dx.doi.org/10.1016/j.frl.2014.12.003
dc.description.abstractWe propose a powerful wavelet method to identify structural breaks in the mean of a process. If there is a structural change in the mean the sum of the squared scaling coefficients absorbs more variation leading to unequal weights for the variances of the wavelet and scaling coefficients. We use this feature of wavelets to design a statistical test for changes in the mean of an independently distributed process. We establish the limiting null distribution of our test and demonstrate that our test has good empirical size and substantive power relative to the existing alternatives especially for multiple breaks. (C) 2014 Elsevier Inc. All rights reserved.
dc.language.isoEnglish
dc.publisherAcademic Press Inc Elsevier Science
dc.subjectStructural change tests
dc.subjectStructural break tests
dc.subjectWavelets
dc.subjectMaximum overlap discrete wavelet
dc.subjectTransformation
dc.titleDetecting structural changes using wavelets
dc.typeArticle
dc.identifier.startpage23
dc.identifier.endpage37
dc.relation.journalFinance Research Letters
dc.identifier.volume12
dc.identifier.wosWOS:000349511700005
dc.identifier.doi10.1016/j.frl.2014.12.003
dc.contributor.khasauthorYazgan, M. Ege


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