Forecasting Gross Domestic Product per Capita Using Artificial Neural Networks with Non-Economical Parameters

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dc.contributor.authorTumer, Abdullah Erdal
dc.contributor.authorAkkus, Aytekin
dc.date.accessioned2024-02-23T14:13:24Z
dc.date.available2024-02-23T14:13:24Z
dc.date.issued2018
dc.departmentNEÜen_US
dc.description.abstractGross Domestic Product per capita is one of the most important indicators of social welfare. All countries try to increase their Gross Domestic Product per capita to contribute to their population's happiness and well-being, as well as strengthen their nation's standing in international relations. Economic growth is affected by economic parameters such as trade, import, and export. However, Gross Domestic Product may also be affected by non-economic factors. Therefore, for a country to increase its Gross Domestic Product per capita, it is important to employ the correct strategy. The aim of this study is to investigate the predictability of Gross Domestic Product per capita based on non-economic data by using artificial neural network with feed forward back-propagation learning algorithm. For this purpose, neural network models have been developed with different architectures. Education level, number of published academic paper per capita, number of researchers per employed, percentage of Research and Development expenditure in the Gross Domestic Product and number of patents per capita are used as input data in the models. The input data has been collected from variety of resources such as Organisation for Economic Cooperation and Development. A comparison between the model results and actual data give a high correlation coefficient (R-2 = 0.96) and show that the model is able to predict the Gross Domestic Product per capita from non-economic parameters. (C) 2018 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.physa.2018.08.047
dc.identifier.endpage473en_US
dc.identifier.issn0378-4371
dc.identifier.issn1873-2119
dc.identifier.scopus2-s2.0-85051394932en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage468en_US
dc.identifier.urihttps://doi.org/10.1016/j.physa.2018.08.047
dc.identifier.urihttps://hdl.handle.net/20.500.12452/12426
dc.identifier.volume512en_US
dc.identifier.wosWOS:000446151000039en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Bven_US
dc.relation.ispartofPhysica A-Statistical Mechanics And Its Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectForecastingen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectGdp Per Capitaen_US
dc.subjectModelsen_US
dc.subjectEconomicen_US
dc.titleForecasting Gross Domestic Product per Capita Using Artificial Neural Networks with Non-Economical Parametersen_US
dc.typeArticleen_US

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