Predicting High Technology Exports of Countries for Sustainable Economic Growth by Using Machine Learning Techniques: The Case of Turkey

Yonis Gulzar (), Ceren Oral (), Mehmet Kayakus, Dilsad Erdogan, Zeynep Unal, Nisa Eksili and Pınar Celik Caylak
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Yonis Gulzar: Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Ceren Oral: Department of Economics and Finance, Fethiye Faculty of Business Administration, Muğla Sıtkı Koçman University, 48300 Fethiye, Turkey
Mehmet Kayakus: Department of Management Information Systems, Faculty of Manavgat Social Sciences and Humanities, Akdeniz University, 07070 Antalya, Turkey
Dilsad Erdogan: Department of Finance, Banking and Insurance, Korkuteli Vocational School, Akdeniz University, 07070 Antalya, Turkey
Zeynep Unal: Department of Biosystem Engineering, Niğde Ömer Halisdemir University, Central Campus, 51240 Niğde, Turkey
Nisa Eksili: Department of Aviation Management, Faculty of Applied Sciences, Akdeniz University, 07058 Antalya, Turkey
Pınar Celik Caylak: Department of Tourism Management, Serik Faculty of Business Administration, Akdeniz University, 07058 Antalya, Turkey

Sustainability, 2024, vol. 16, issue 13, 1-20

Abstract: In this study, the estimation of high-tech exports for Turkey’s foreign trade target in line with sustainable development was carried out. The research was carried out for Turkey since it has been focusing on sustainable and environmentally friendly production and an export-oriented growth model, with a transformation in its economic growth strategy as of 2021, and high-tech products are a determining factor in the export target. In this research, three different machine learning techniques, namely artificial neural networks, logistic regression, and support vector regression, were used to determine a successful prediction method close to the ideal scenario. In the models, high technology exports for the period of 2007–2023 with data obtained from the World Bank were taken as the dependent variable, while the gross national product, number of patents, and research and development expenditures were taken as independent variables. By calculating the R 2 , MAPE, and MSE metrics, the success of the model with the least error was evaluated, and it was seen that artificial neural networks (ANNs) were the most successful model, with values of 94.2%, 0.011, and 0.073, respectively. The ANN model was followed by support regression and logistic regression.

Keywords: technology; export; economy; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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