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dc.contributor.advisorAlemdar, Nedimen_US
dc.contributor.authorCoşkun, Yeşimen_US
dc.date.accessioned2016-01-08T18:08:15Z
dc.date.available2016-01-08T18:08:15Z
dc.date.issued2008
dc.identifier.urihttp://hdl.handle.net/11693/14800
dc.descriptionAnkara : The Department of Economics, The Institute of Economics and Social Sciences of Bilkent University, 2008.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2008.en_US
dc.descriptionIncludes bibliographical references leaves 27.en_US
dc.description.abstractThis thesis work presents a direct numerical solution methodology to approximate the small open economy models with debt elastic interest rate premium and with convex portfolio adjustment cost, both studied by Stephanie SchmittGroh´e and Martin Uribe(2003). This recent method is compared with the firstorder approximation to the policy function from the aspect of second moments of endogenous variables and their impulse responses. The proposed methodology, namely genetic algorithm-neural network (GA-NN), parameterizes the policy function with a feed-forward neural network that is trained by a genetic algorithm. Thus, unlike the first-order approximation, GA-NN does not require the continuity and the existence of derivatives of objective and policy functions. Importantly, since genetic algorithm is an evolutionary algorithm that enables global search over the feasible set, it provides a robust result in any solution space. Also GA-NN method gives not only the moments of the model but also the optimal path.en_US
dc.description.statementofresponsibilityCoşkun, Yeşimen_US
dc.format.extentx, 46 leavesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGenetic Algorithmen_US
dc.subjectPortfolio Adjustment Costen_US
dc.subjectDebt-elastic Interest Rateen_US
dc.subjectNeural Networken_US
dc.subject.lccHG1621 .C67 2008en_US
dc.subject.lcshInterest rates--Mathematical models.en_US
dc.subject.lcshEconometric models.en_US
dc.subject.lcshPortfolio management.en_US
dc.subject.lcshGenetic algorithms.en_US
dc.subject.lcshNeural networks (Computer science)en_US
dc.titleApproximating small open economy models with neural network trained by genetic algorithmen_US
dc.typeThesisen_US
dc.departmentDepartment of Economicsen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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