In a globalizing world, forecasts of economic variables for the coming years are of great importance. Purchasing power parity makes countries' currencies equal to purchasing power and is used as a criterion when comparing countries economically. In this study, estimation of gross domestic product per capita according to purchasing power parity for 12 developed and developing countries was determined by artificial neural networks and regression method. Mean absolute percent error values of the data found for the two methods were calculated. For 2018, the actual values are compared with the estimated data. As a result, it was found that artificial neural networks given more significant results than regression. For next years, both methods were estimated.
Forecasting Gross Domestic Product Neural Network Regression
Gayrisafi Yurt İçi Hasıla Yapay Sinir Ağı Regresyon Tahminleme
Birincil Dil | Türkçe |
---|---|
Bölüm | Araştırma Makaleleri |
Yazarlar | |
Yayımlanma Tarihi | 25 Haziran 2020 |
Kabul Tarihi | 4 Mayıs 2020 |
Yayımlandığı Sayı | Yıl 2020 Sayı: 3 |
Uluslararası Sosyal Bilimler Akademi Dergisi (USBAD), İnönü Üniversitesi Eğitim Fakültesi Türkçe ve Sosyal Bilimler Eğitimi Bölümü Yerleşke / Malatya
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