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dc.contributor.advisorSayan, Serdaren_US
dc.contributor.authorUçar, Nurien_US
dc.date.accessioned2016-01-08T18:08:02Z
dc.date.available2016-01-08T18:08:02Z
dc.date.issued2001
dc.identifier.urihttp://hdl.handle.net/11693/14786
dc.descriptionAnkara : The Department of Economics, Bilkent University, 2001.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2001.en_US
dc.descriptionIncludes bibliographical refences.en_US
dc.description.abstractThis thesis compares a variety of linear and nonlinear models to find the one with the best inflation forecast performance for the Turkish Economy. These comparisons are performed by considering the type of series whether or not stationary. Different combination techniques are applied to improve the forecasts. It is observed that the combination forecasts based on nonstationary vector autoregressive (VAR) and artificial neural network (ANN) models are better than the ones generated by other models. Furthermore, the forecast values combined with ANN technique produce lower root mean square errors (RMSE) than the other combination techniques.en_US
dc.description.statementofresponsibilityUçar, Nurien_US
dc.format.extent40 leaves, tablesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial Neural Networken_US
dc.subjectInflationen_US
dc.subjectForecasten_US
dc.subjectTime Seriesen_US
dc.subject.lccHC492 .U23 2001en_US
dc.subject.lcshEconomic forecasting--Turkey--Mathematical models.en_US
dc.subject.lcshEconometrics.en_US
dc.subject.lcshTime-series analysis.en_US
dc.subject.lcshNeural networks (Computer sciences).en_US
dc.subject.lcshInflation (Finance)--Turkey.en_US
dc.titleComparison of the forecast performances of linear time series and artificial neural network models within the context of Turkish inflationen_US
dc.typeThesisen_US
dc.departmentDepartment of Economicsen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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