A COMPARATIVE ANALYSIS OF ALTERNATIVE UNIVARIATE TIME SERIES MODELS IN FORECASTING TURKISH INFLATION

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dc.contributor.authorCatik, A. Nazif
dc.contributor.authorKaracuka, Mehmet
dc.date.accessioned2019-10-27T21:43:44Z
dc.date.available2019-10-27T21:43:44Z
dc.date.issued2012
dc.departmentEge Üniversitesien_US
dc.description.abstractThis paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at earlier forecast horizons conventional models, especially ARFIMA and ARIMA, provide better one-step ahead forecasting performance. However, unobserved components model turns out to be the best performer in terms of dynamic forecasts. The superiority of the unobserved components model suggests that inflation in Turkey has time varying pattern and conventional models are not able to track underlying trend of inflation in the long run.en_US
dc.identifier.doi10.3846/16111699.2011.620135
dc.identifier.endpage293en_US
dc.identifier.issn1611-1699
dc.identifier.issn1611-1699en_US
dc.identifier.issue2en_US
dc.identifier.startpage275en_US
dc.identifier.urihttps://doi.org/10.3846/16111699.2011.620135
dc.identifier.urihttps://hdl.handle.net/11454/47137
dc.identifier.volume13en_US
dc.identifier.wosWOS:000302497900005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherVilnius Gediminas Tech Univen_US
dc.relation.ispartofJournal of Business Economics and Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectinflation forecastingen_US
dc.subjectneural networksen_US
dc.subjectunobserved components modelen_US
dc.titleA COMPARATIVE ANALYSIS OF ALTERNATIVE UNIVARIATE TIME SERIES MODELS IN FORECASTING TURKISH INFLATIONen_US
dc.typeArticleen_US

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