FORECASTING GROSS DOMESTIC PRODUCT (GDP) OF OECD COUNTRIES BASED ON DIGITAL TRANSFORMATION INDICATORS: ANN APPROACH

The ongoing COVID-19 pandemic process we all face currently has highlighted the fact that digitalization of economic activities is very crucial. Digital economy became even more important after the pandemic. When the governments allocate a greater proportion of Gross Domestic Product on some public services, the lower the proportion spent on other goods and services. For this reason, many countries want to estimate the Gross Domestic Product spent on public services within the scope of public control and risk management. The main goal in this study is to predict Gross Domestic Product per capita based on digital indicators such as fixed-broadband subscriptions, mobile-cellular telephone penetration rate, fixed-telephone subscriptions, internet penetration rate, research and development expenditure and patent applications. Artificial Neural Networks are employed to predict Gross Domestic Product per capita via digital indicators. Artificial Neural Network method is used to estimate the digital economy for 29 OECD countries between 2002–2018. The results demonstrate that Artificial Neural Networks can be utilized effectively in applications of digital economy forecasting. According to the results, coefficient of determination (R2 = 0.92) is very high, meaning that Artificial Neural Networks can be effectively utilized in Gross Domestic Product per capita estimations through digital indicators. This article differs from previous studies as it uses digital economic indicators in predicting Gross Domestic Product per capita by applying Artificial Neural Networks model.