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Article: Likelihood ratio tests for the structural change of an AR(p) model to a Threshold AR(p) model

TitleLikelihood ratio tests for the structural change of an AR(p) model to a Threshold AR(p) model
Authors
KeywordsStructure change
Two-parameter Gaussian process
Threshold AR(p)
AR(p)
Likelihood ratio test
Issue Date2012
Citation
Journal of Time Series Analysis, 2012, v. 33, n. 2, p. 223-232 How to Cite?
AbstractThis article considers the likelihood ratio (LR) test for the structural change of an AR model to a threshold AR model. Under the null hypothesis, it is shown that the LR test converges weakly to the maxima of a two-parameter vector Gaussian process. Using the approach in Chan and Tong (1990)and Chan (1991), we obtain a parameter-free limiting distribution when the errors are normal. This distribution is novel and its percentage points are tabulated via a Monte Carlo method. Simulation studies are carried out to assess the performance of the LR test in the finite sample and a real example is given. © 2011 Blackwell Publishing Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/230889
ISSN
2015 Impact Factor: 1.0
2015 SCImago Journal Rankings: 1.177

 

DC FieldValueLanguage
dc.contributor.authorZhu, Ke-
dc.contributor.authorLing, Shiqing-
dc.date.accessioned2016-09-01T06:07:04Z-
dc.date.available2016-09-01T06:07:04Z-
dc.date.issued2012-
dc.identifier.citationJournal of Time Series Analysis, 2012, v. 33, n. 2, p. 223-232-
dc.identifier.issn0143-9782-
dc.identifier.urihttp://hdl.handle.net/10722/230889-
dc.description.abstractThis article considers the likelihood ratio (LR) test for the structural change of an AR model to a threshold AR model. Under the null hypothesis, it is shown that the LR test converges weakly to the maxima of a two-parameter vector Gaussian process. Using the approach in Chan and Tong (1990)and Chan (1991), we obtain a parameter-free limiting distribution when the errors are normal. This distribution is novel and its percentage points are tabulated via a Monte Carlo method. Simulation studies are carried out to assess the performance of the LR test in the finite sample and a real example is given. © 2011 Blackwell Publishing Ltd.-
dc.languageeng-
dc.relation.ispartofJournal of Time Series Analysis-
dc.subjectStructure change-
dc.subjectTwo-parameter Gaussian process-
dc.subjectThreshold AR(p)-
dc.subjectAR(p)-
dc.subjectLikelihood ratio test-
dc.titleLikelihood ratio tests for the structural change of an AR(p) model to a Threshold AR(p) model-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1467-9892.2011.00753.x-
dc.identifier.scopuseid_2-s2.0-84858003749-
dc.identifier.volume33-
dc.identifier.issue2-
dc.identifier.spage223-
dc.identifier.epage232-
dc.identifier.eissn1467-9892-

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