Türkiye’de Ekonomik Karmaşıklık Endeksinin Bulanık Sinir Ağları Yöntemiyle Tahmini

Abstract

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.

Keywords

• Ekonomik Karmaşıklık Endeksi
• Yapay Zeka
• Bulanık Sinir Ağları

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

Abstract

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.

Keywords

• Economic Complexity Index
• Artificial Intelligence
• Fuzzy Neural Networks

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