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dc.contributor.advisorAlemdar, Nedimen_US
dc.contributor.authorKıykaç, Cihanen_US
dc.date.accessioned2016-07-01T11:06:14Z
dc.date.available2016-07-01T11:06:14Z
dc.date.issued2006
dc.identifier.urihttp://hdl.handle.net/11693/29829
dc.descriptionCataloged from PDF version of article.en_US
dc.description.abstractIn this thesis study, we present a direct numerical solution methodology for the onesector nonlinear stochastic growth model. Rather than parameterizing or dealing with the Euler equation, like other methods do, our method directly parameterizes the policy function with a neural network trained by a genetic algorithm. Since genetic algorithms are derivative free and the policy function is directly parameterized, there is no need for taking derivatives. While other methods are bounded by the existence of required derivatives in higher dimensional state spaces, our method preserves its functionality. As genetic algorithms are global search algorithms, our method’s results are robust whatever the search space is. In addition to the stochastic growth model, to observe the performance of the method under real conditions, we tested the method by adding capital adjustment costs to the model. Under all parameter configurations, the method performs quite well.en_US
dc.description.statementofresponsibilityKıykaç, Cihanen_US
dc.format.extentxii, 72 leaves, graphicsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectStochastic growth modelen_US
dc.subjectNeural networksen_US
dc.subjectGenetic algorithmsen_US
dc.subject.lccQA274 .K59 2006en_US
dc.subject.lcshStochastic processes Mathematical models.en_US
dc.titleApproximating the stochastic growth model with neural networks trained by genetic algorithmsen_US
dc.typeThesisen_US
dc.departmentDepartment of Economicsen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidBILKUTUPB098896


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