Türkiye’de Ekonomik Karmaşıklık Endeksinin Bulanık Sinir Ağları Yöntemiyle Tahmini

Öz

Ekonomik karmaşıklık, ülkelerin ihracatlarında bulunan üretim yeteneklerinin çeşitliliğini ve karmaşıklık düzeyini ifade eder. Daha karmaşık bir ekonomik yapı, ülkelerin verimliliği yüksek faaliyetlere yönelerek daha hızlı kalkınmalarına olanak tanır. Karmaşıklık düzeyi yüksek olan ülkeler aynı zamanda uluslararası pazarlarda rekabet üstünlüğüne sahiptir. Ekonomik Karmaşıklık Endeksi'nin, ülkeler arasındaki gelir farklılıklarının açıklanmasında etkili olduğu ve gelecekteki büyümeyi diğer göstergelere kıyasla daha iyi öngördüğü ortaya konmuştur. Bu çerçevede ekonomik karmaşıklığı etkileyen faktörleri belirlemek günümüz ekonomileri açısından oldukça önemlidir. Bu çalışmada, 1995-2022 yıllarına ait veriler kullanılarak Türkiye’de ekonomik karmaşıklığı etkileyen faktörler, yapay zekanın bir alt dalı olan bulanık sinir ağları yöntemi ile tahmin edilmiştir. Tahmin edilen ekonomik karmaşıklık değerleri ile gerçekleşen değerler arasındaki hata oranının düşük olması, önerilen bulanık sinir ağları yönteminin ekonomik karmaşıklığın tahmininde etkili bir şekilde kullanılabileceğini göstermektedir.

Anahtar Kelimeler

• Ekonomik Karmaşıklık Endeksi
• Yapay Zeka
• Bulanık Sinir Ağları

Estimation of Economic Complexity Index in Türkiye Using Fuzzy Neural Networks Method

Öz

Economic complexity reflects the variety and sophistication of a country's production capabilities as manifested in its exports. A more complex economic structure allows countries to develop faster by focusing on high-productivity activities. Countries with high economic complexity also tend to hold a competitive edge in global markets. It has been shown that the Economic Complexity Index is effective in explaining income differences between countries and predicts future growth more accurately than other indicators. In this context, determining the factors affecting economic complexity is very important for today's economies. In this study, the factors affecting economic complexity in Türkiye have been estimated by using the fuzzy neural networks method, which is a sub-branch of artificial intelligence, using data from 1995-2022. The low error rate between the predicted and actual economic complexity values shows that the proposed fuzzy neural networks method can be used effectively in the estimation of economic complexity.

Anahtar Kelimeler

• Economic Complexity Index
• Artificial Intelligence
• Fuzzy Neural Networks

Kaynakça

1. Abraham, A. (2005). Artificial Neural Networks and Intelligent Systems. In Handbook of Measuring System Design. John Wiley & Sons. https://doi.org/10.1002/0471497398.mm421
2. Acemoğlu, D., Johnson, S., and Robinson, J.A. (2001). The Colonial Origins of Comparative Development: An Empirical Investigation. American Economic Review, 91(5), 1369 1401.
3. Alaya, M. (2012). The Determinants of MENA Export Diversification: An Emprical Analysis. ERF Working Paper Series.
4. Athey, S. (2018). The Impact of Machine Learning on Economics. In The Economics of Artificial İntelligence: An agenda (507–547). University of Chicago Press.
5. Bacanlı, Ü.G., Dikbaş, F. ve Fırat, M. (2011). Sinir Ağları ve Bulanık Mantık Yöntemleri ile Kuraklık Tahmini, Pamukkale Üniversitesi Bilimsel Araştırma Yapay Projeleri Koordinasyon Birimi. Bap Projesi.
6. Bahar, D., Rapoport, H., and Turati, R (2022). Birthplace Diversity and Economic Complexity: Cross-Country Evidence. Research Policy, 51, 103991.
7. Baykal, N. ve Beyan, T. (2004), Bulanık Mantık Uzman Sistemler ve Denetleyiciler. Bıçaklar Kitabevi, Ankara.
8. Bucak, Ç. (2021). AB15 Ülkelerinde ve Türkiye’de Ekonomik Karmaşıklık Endeksi, İnsani Gelişme Endeksi ve Karbon Emisyonu: Panel Veri Analizi. Ege Stratejik Araştırmalar Dergisi, 12(1), 71–88.

9. Chang, F.J. and Chang, Y.T. (2006). Adaptive Neuro-Fuzzy Inference System for Prediction of Water Level in Reservoir. Advances in Water Resources, 29, 1-10.
10. Costinot, A. (2009). On the Origins of Comparative Advantage. Journal of International Economics, 77(2), 255–264.
11. Doru, Ö. (2022). Türkiye’de Ekonomik Karmaşıklık Endeksi ile Doğrudan Yabancı Yatırım İlişkisi. Artuklu Kaime Uluslararası İktisadi ve İdari Araştırmalar Dergisi, 5(2), 235–251.
12. Erkan, B. and Yıldırımcı. E (2015). Economic Complexity and Export Competitiveness: The Case of Turkey. Procedia -Social and Behavioral Sciences, 195, 524-533.
13. Felipe, J., Kumar, U., Abdon, A., and Bacate, M. (2012). Product Complexity and Economic Development. Structural Change and Economic Dynamics, 23(1), 36-68.
14. Gala, P., Camargo, J.S.M., Magacho, G., and Rocha, I. (2018). Sophisticated Jobs Matter For Economic Complexity: An Empirical Analysis Based on Input-Output Matrices and Employment Data. Structural Change and Economic Dynamics, 45, 1-8.
15. Güneş, S., Gürel, S.P., Karadam, D.Y., and Akın, T. (2020). The Analysis of Main Determinants of High Technology Exports: A Panel Data Analysis. KAUJEASF, 11(21), 242 267.
16. Harvad Growth Lab. Atlas of Economic Complexity (2020). Atlas of Economic Complexity. https://atlas.cid.harvard.edu/rankings (E.T.:05.09.2024)
17. Hausmann, R. and Hidalgo, C.A. (2011). The Network Structure of Economic Output. Journal of Economic Growth, 16(4), 309-342.
18. Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Prentice Hall.
19. Hidalgo, C.A. (2021). Economic Complexity Theory and Applications. Nature Reviews Physics, 3(2), 92-113.
20. Hidalgo, C.A. and Hausmann, R. (2009). The Building Blocks of Economic Complexity. Proceedings of the National Academy of Sciences. 106(26), 10570-10575.
21. Inoua, S. (2016). A simple measure of economic complexity. Economics Letters, 149, 118–121. https://doi.org/10.1016/j.econlet.2016.10.008
22. Jang, J. S. R. (1993). ANFIS: Adaptive-network-Based Fuzzy İnference System. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685.
23. Lapatinas, A. and Litina, A. (2019). Intelligence and Economic Sophistication. Empirical Economics 57, 1731-50.
24. Lapatinas, A., Kyriakou, A., and Garas, A. (2019), Taxation and Economic Sophistication: Evidence from OECD Countries. PLoS ONE 14(3), https://doi.org/10.1371 /journal.pone.0213498
25. Lester, J.M. (2003). Investigation of the Applicability of Neural- Fuzzy Logic Modeling for Culvert Hydrodynamics. Doctor of Philosophy Thesis, College of Engineering and Mineral Resources at West Virginia University,
26. Nauck, D. and Kruse, R. (1999). Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine, 16(2), 149–169.
27. Nayak, P.C., Sudheer, K.P., and Ramasastri, K.S. (2004). Fuzzy Computing Based Rainfall-Runoff Model For Real Time Flood Forecasting. Hydrological Process, 17, 3749–3762.
28. Nguyen, C.P., Schinckus, C., and Su, T.D. (2020). The Drivers of Economic Complexity: International Evidence From Financial Development and Patents. International Economics, 164, 140–50.
29. Rivera, B., Leon, M., Cornejo, G., and Florez, H. (2023). Analysis of the Effect of Human Capital, Institutionality and Globalization on Economic Complexity: Comparison between Latin America and Countries with Greater Economic Diversification. Economies, 11 (18), 1-16.
30. Spolaore, E. and Wacziarg, R. (2013). How Deep are the Roots of Economic Development?. Journal of Economic Literature, 51(2), 325-369.
31. Stojkoski, V., Utkovski, Z., and Kocarev, L. (2016). The Impact of Services on Economic Complexity: Service Sophistication As Route For Economic Growth. PLoS ONE, 11(8), e0161633.
32. Şeker, A. (2019). Teknolojik Gelişme ve Yüksek Teknoloji İhracatının Ekonomik Karmaşıklık Endeksi Üzerindeki Etkisi: Türkiye Örneği. Yönetim ve Ekonomi, 26(2), 377-395.
33. Şeker, A. and Şimdi, H. (2019). The_Relationship_Between_Economic_Complexity Index and Export: the Case of Turkey and Central Asian and Turkic Republics. Ekonomika Regiona, 15(3), 659-669.
34. Takagi, T. and Sugeno, M. (1985). Fuzzy Identification of Systems and Its Application to Modeling and Control. IEEE Transactions on Systems, Man, and Cybernetics, 15, 116-132.
35. Vu, T.V. (2021). Statehood Experience and Income Inequality: A Historical Perspective. Economic Modelling, 94, 415-429.
36. Vu, T.V. (2022). Does Institutional Quality Foster Economic Complexity? The Fundamental Drivers of Productive Capabilities. Empirical Economics, 63 (1), 1-35
37. Yalta Y.A. and Yalta, T. (2021). Determinants of Economic Complexity in MENA Countries. Journal of Emerging Economies and Policy, 6(1), 5-16.
38. Yıldırım, E., Avcı, E. ve Yılmaz, B. (2021). Serbest Basınç Dayanımının Tahmininde Sugeno Bulanık Mantık Yaklaşımı. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 26(1), 97-108.
39. Zadeh, L. A. (1996). Fuzzy Logic = Computing With Words. IEEE Transactions on Fuzzy Systems, 4(2), 103–111.