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dc.contributor.authorKapetanios, Georgeen
dc.date.accessioned2012-09-20T12:59:07Z-
dc.date.available2012-09-20T12:59:07Z-
dc.date.issued2002-
dc.identifier.urihttp://hdl.handle.net/10419/62809-
dc.description.abstractIn this paper we suggest a number of statistical tests based on neural network models, that are designed to be powerful against structural breaks in otherwise stationary time series processes while allowing for a variety of nonlinear specifications for the dynamic model underlying them. It is clear that in the presence of nonlinearity standard tests of structural breaks for linear models may not have the expected performance under the null hypothesis of no breaks because the model is misspecified. We therefore proceed by approximating the conditional expectation of the dependent variable through a neural network. Then, the residual from this approximation is tested using standard residual based structural break tests. We investigate the asymptoptic behaviour of residual based structural break tests in nonlinear regression models. Monte Carlo evidence suggests that the new tests are powerful against a variety of structural breaks while allowing for stationary nonlinearities.en
dc.language.isoengen
dc.publisher|aQueen Mary University of London, Department of Economics |cLondonen
dc.relation.ispartofseries|aWorking Paper |x470en
dc.subject.jelC22en
dc.subject.jelC12en
dc.subject.jelC45en
dc.subject.ddc330en
dc.subject.keywordNonlinearity, Structural breaks, Neural networksen
dc.subject.stwStrukturbruchen
dc.subject.stwNeuronale Netzeen
dc.subject.stwNichtlineare dynamische Systemeen
dc.titleTesting for structural breaks in nonlinear dynamic models using artificial neural network approximations-
dc.typeWorking Paperen
dc.identifier.ppn377024384en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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