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dc.contributor.authorKapetanios, Georgeen_US
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_US
dc.publisher|aQueen Mary, Univ. of London, Dep. of Economics |cLondonen_US
dc.relation.ispartofseries|aWorking Paper, Department of Economics, Queen Mary, University of London |x470en_US
dc.subject.keywordNonlinearity, Structural breaks, Neural networksen_US
dc.subject.stwNeuronale Netzeen_US
dc.subject.stwNichtlineare dynamische Systemeen_US
dc.titleTesting for structural breaks in nonlinear dynamic models using artificial neural network approximationsen_US
dc.typeWorking Paperen_US

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