Performance Comparison of Machine Learning Methods in Trade Forecasting within the Framework of the Gravity Model: The Case of Turkey and the Turkic Republics

Yıl 2024, Cilt: 31 Sayı: 3, 439 - 459, 27.09.2024

Ahmet Ay , Haldun Soydal , Mustafa Ay

https://doi.org/10.18657/yonveek.1520642

Öz

This study aims to analyze Turkey's trade volume with the Turkic Republics (Azerbaijan, Kazakhstan, Kyrgyzstan, Uzbekistan, and Turkmenistan) within the framework of the international trade gravity model and to predict the trade volume with these countries for the years 2024 and 2025. Export and import data between Turkey and the Turkic Republics from 1992 to 2023 were used, with countries' GDPs and distances as numerical variables, and border, language, landlocked status, and World Trade Organization (WTO) memberships as dummy variables. These data were processed using different machine learning models, including Linear Regression, Gaussian Process Regression, and Multilayer Perceptron, to predict trade volumes. The success of the applied machine learning models was compared based on MAPE (Mean Absolute Percentage Error) values. The analysis results indicated that the Multilayer Perceptron model provided the most accurate predictions. This finding demonstrates the effectiveness of advanced machine learning methods in understanding complex trade dynamics and forecasting future trade trends. A better understanding of trade relations between Turkey and the Turkic Republics and predicting the future trajectory of these relations will significantly contribute to the formulation of regional economic policies. The study also provides valuable insights for developing strategies to enhance trade with these countries.
Key Words: Gaussian Process Regression, Gravity Model, Machine Learning, Multilayer Perceptron, Turkic Republics.
JEL Classification: C51, C53, F17

Anahtar Kelimeler

Gaussian Process Regression, Gravity Model, Machine Learning, Multilayer Perceptron, Turkic Republics

Çekim Modeli Çerçevesinde Ticaret Tahmininde Makine Öğrenmesi Yöntemlerinin Performans Karşılaştırması: Türkiye ve Türk Cumhuriyetleri Örneği

Öz

Bu çalışmada, Türkiye'nin Türk Cumhuriyetleri (Azerbaycan, Kazakistan, Kırgızistan, Özbekistan ve Türkmenistan) ile olan ticaret hacmi çekim modeli kullanılarak analiz edilmiş ve 2024-2025 yılları için Türkiye ile bu ülkeler arasındaki ticaret hacmini tahmin etmede en başarılı makine öğrenimi yöntemi belirlenmek istenmiştir. Bu amaçla çalışmada Türk Cumhuriyetlerinin bağımsızlıklarını kazandıkları 1992 yılından başlayarak 2023 yılına kadar olan veriler kullanılmıştır. Bu veriler sayısal değişkenler olarak Türkiye ile Türk Cumhuriyetleri arasındaki ihracat ve ithalat verileri, ülkelerin milli gelirleri, aralarındaki mesafe; kukla değişkenler olarak ise ülkelerin birbirleriyle olan sınırı, ortak dil, ülkelerin karayla çevrililik durumu ve Dünya Ticaret Örgütü (DTÖ) üyelikleridir. Bu veriler, ticaret hacmini tahmin etmek için Lineer Regresyon, Gauss Süreç Regresyonu ve Çok Katmanlı Algılayıcılar gibi farklı makine öğrenmesi modelleri ile analiz edilmiştir. Uygulanan makine öğrenmesi modellerinin başarısı MAPE (Ortalama Mutlak Yüzde Hata) değerleri üzerinden kıyaslanmıştır. Analiz sonuçları, Çok Katmanlı Algılayıcılar modelinin en doğru tahminleri sağladığını ortaya koymuştur. Bu durum, ileri düzey makine öğrenmesi yöntemlerinin karmaşık ticaret dinamiklerini anlamada ve gelecekteki ticaret eğilimlerini öngörmede ne kadar etkili olabileceğini göstermektedir. Türkiye ve Türk Cumhuriyetleri arasındaki ticaret ilişkilerinin daha iyi anlaşılması ve bu ilişkilerin gelecekteki seyrinin tahmin edilmesi, bölgesel ekonomik politikaların oluşturulmasında önemli katkılar sağlayacaktır. Çalışma, bu ülkelerle olan ticaretin gelişimine yönelik stratejilerin belirlenmesi açısından da önemlidir.
Anahtar Kelimeler: Çekim Modeli, Çok Katmanlı Algılayıcılar, Gauss Süreç Regresyonu, Makine Öğrenmesi, Türk Cumhuriyetleri.
JEL Sınıflandırması: C51, C53, F17

Anahtar Kelimeler

Çekim Modeli, Çok Katmanlı Algılayıcılar, Gauss Süreç Regresyonu, Makine Öğrenmesi, Türk Cumhuriyetleri

Kaynakça

• Aktaş, Hayati-İpek, Cemil Doğaç, (2014), “Frankofoni (Fransızca Konuşan Ülkeler Topluluğu) Türk Dünyası İçin Bir Model Olabilir mi?”, Türk Diasporası ve Türk Dünyası Vizyon 2023, Editör: Almagül İsina, Tasam Yayınları, İstanbul, ss. 45-48. https://tasam.org/tr-TR/Icerik/53597/frankofoni_fransizca_konusan_ulkeler_toplulugu_turk_dunyasi_icin_bir_model_olabilir_mi
• Alpaydin, E. (2014). Introduction to Machine Learning, third edition. MIT Press. https://books.google.com.tr/books?id=7f5bBAAAQBAJ
• Anderson, J. E., & van Wincoop, E. (2003). Gravity with Gravitas: A Solution to the Border Puzzle American Economic Review, 93(1), 170–192. https://doi.org/10.1257/000282803321455214
• Baier, S. L., & Bergstrand, J. H. (2007). Do free trade agreements actually increase members’ international trade? Journal of International Economics, 71(1), 72–95. https://doi.org/https://doi.org/10.1016/j.jinteco.2006.02.005
• Bangdiwala, S. I. (2018). Regression: simple linear. International Journal of Injury Control and Safety Promotion, 25(1), 113–115. https://doi.org/10.1080/17457300.2018.1426702
• Batarseh, F., Gopinath, M., Nalluru, G., & Beckman, J. (2019). Application of machine learning in forecasting international trade trends. ArXiv Preprint ArXiv:1910.03112.
• Ben Ameur, H., Boubaker, S., Ftiti, Z., Louhichi, W., & Tissaoui, K. (2023). Forecasting commodity prices: Empirical evidence using deep learning tools. Annals of Operations Research. Advance online publication. https://doi.org/10.1007/s10479-022-05076-6
• Bergstrand, J. (1985). The Gravity Equation In International Trade: Some Microeconomic Foundations And Empirical Evidence. The Review of Economics and Statistics, 67, 474–481. https://doi.org/10.2307/1925976
• Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Clarendon Press. https://books.google.com.tr/books?id=T0S0BgAAQBAJ Cheremisin, A., Usov, E., Kolchanov, B., Krylov, A., Valkovich, A., Lykhin, P., Ulyanov, V., Khogoeva, E., &
• Podnebesnykh, A. (2022). Mathematical justification for optimizing operating conditions of gas and gas condensate producing wells. Energies, 15(10), 3676. https://doi.org/10.3390/en15103676
• Circlaeys, S., Kanitkar, C., & Kumazawa, D. (2017). Bilateral trade flow prediction. Unpublished Manuscript, Available for Download at Http://Cs229. Stanford. Edu/Proj2017/Final-Reports/5240224. Pdf.
• Goldfarb, A., & Trefler, D. (2018). Artificial Intelligence,and International Trade. The Economics of Artificial Intelligence: An Agenda, 463–492.
• Helpman, E., Melitz, M., & Rubinstein, Y. (2008). Estimating Trade Flows: Trading Partners and Trading Volumes. The Quarterly Journal of Economics, 123(2), 441–487. https://econpapers.repec.org/RePEc:oup:qjecon:v:123:y:2008:i:2:p:441-487.
• Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/https://doi.org/10.1016/j.ijforecast.2006.03.001
• International Crisis Group (ICG). (2015). Central Asia Reports.
• International Energy Agency (IEA). (2015). World Energy Outlook. International Monetary Fund (IMF). (2015). Regional Economic Outlook: Middle East and Central Asia.
• Jošić, H., & Žmuk, B. (2022). A Machine Learning Approach to Forecast International Trade: The Case of Croatia. Business Systems Research, 13(3), 144–160. https://doi.org/10.2478/bsrj-2022-0030
• Korkmaz, M., Dogan, A., & Kirmaci, V. (2022). Performance Analysis of Counterflow Ranque – Hilsch Vortex Tube with Linear Regression, Support Vector Machines and Gaussian Process Regression Method. 361–370. https://doi.org/10.30855/gmbd.0705015
• Kottou, E. M., Grubelich, T. A., & Wang, X. (2020). Bilateral Trade Flow Prediction Models Enhanced By Wavelet and Machine Learning Algorithms. 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 1510–1516. https://doi.org/10.1109/CSCI51800.2020.00279
• Kulkarni, A., & More, A. (2016). Formulation of a Prediction Index with the Help of WEKA Tool for Guiding the Stock Market Investors. Oriental Journal of Computer Science and Technology, 9, 212–225. https://doi.org/10.13005/ojcst/09.03.07 Kuşkapan E. ve Çodur M. Y. “Trafik kazalarının sınıflandırılmasında çok katmanlı algılayıcı, regresyon ve en yakın komşuluk algoritmalarının performans analizi”, Politeknik Dergisi, 25(1): 373-380, (2022).
• Linnemann, H. (1966). An econometric study of international trade flows [North-Holland Pub. Co.]. https://doi.org/LK - https://worldcat.org/title/239355
• Melitz, J. (2008). Language and foreign trade. European Economic Review, 52(4), 667–699. https://doi.org/https://doi.org/10.1016/j.euroecorev.2007.05.002
• Moisl, Hermann. (2009). Sura Length and Lexical Probability Estimation in Cluster Analysis of the Qur’an. ACM Trans. Asian Lang. Inf. Process.. 8. 10.1145/1644879.1644886.
• Nuroğlu, E. (2012). Estimating Trade Flows of Turkey Using Panel Data Analysis and Neural Networks. Bartın Üniversitesi İİBF Dergisi, 3, 13–34.
• Nyoni, T. (2019). Exports and imports in Zimbabwe: recent insights from artificial neural networks.
• Pöyhönen, P. (1963). A Tentative Model for the Volume of Trade between Countries. Weltwirtschaftliches Archiv, 90, 93–100. http://www.jstor.org/stable/40436776
• Quimba, F. M. A., & Barral, M. A. A. (2018). Exploring neural network models in understanding bilateral trade in APEC: A review of history and concepts (Issues 2018–33). Philippine Institute for Development Studies (PIDS). https://hdl.handle.net/10419/211053
• Rao, P., Baheti, P., & Ramesh, R. (2023). TRADE FLOW ESTIMATION BETWEEN GLOBAL ECONOMIES. International Journal of Advance Computational Engineering and Networking (IJACEN), 11(4), 1–5.
• Rasmussen, C. E. (2004). Gaussian Processes in Machine Learning. In O. Bousquet, U. von Luxburg, & G. Rätsch (Eds.), Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003, Revised Lectures (pp. 63–71). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-28650-9_4
• Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.
• Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536. https://doi.org/10.1038/323533a0
• Snelson, E., & Ghahramani, Z. (2007). Local and global sparse Gaussian process approximations. Journal of Machine Learning Research, 2, 524–531.
• Tinbergen, J. (1962). Shaping the World Economy; Suggestions for an International Economic Policy. http://hdl.handle.net/1765/16826
• UN Comtrade. (2024). https://comtradeplus.un.org/TradeFlow
• Wohl, I., & Kennedy, J. (2018). Neural network analysis of international trade. US International Trade Commission: Washington, DC, USA.
• World Bank. (2015). Kazakhstan Economic Update.
• World Trade Organization (WTO). (2015). World Trade Report.
• Žmuk, B., & Jošić, H. (2020). Forecasting Stock Market Indices Using Machine Learning Algorithms. Interdisciplinary Description of Complex Systems, 18(4), 471–489. https://doi.org/10.7906/indecs.18.4.7