DÜNYA DENİZ TİCARETİ VE TÜRKİYE DIŞ TİCARETİ İLİŞKİLERİ: EKONOMETRİK BİR ANALİZ

Yıl 2019, Cilt: 3 Sayı: 5, 152 - 162, 28.02.2019

Hakan Eryüzlü

https://doi.org/10.30520/tjsosci.524826

Cited By: 4

Öz

Deniz,
denizcilik, deniz taşımacılığı vb. kavramlar Dünya tarihi boyunca önemini
yitirmemiş, medeniyetlerin oluşmasında ve gelişmesinde önemli roller
üstlenmiştir. Dünya mal ticaretinin yaklaşık %75’i deniz yolu ile
yapılmaktadır. Deniz taşımacılığının hala en çok tercih edilen taşımacılık
alternatifi olmasının birçok nedeni bulunmaktadır. Türkiye de toplam ihracat ve
ithalatının yarısından fazlasının deniz yolu ile yapıldığı önemli bir deniz
ülkesidir. Çalışmada, Dünya deniz ticareti ve Türkiye dış ticaret ilişkileri
araştırılmıştır. Bu amaçla, Dünya deniz ticaretini temsilen, baltık kuru yük
endeksi (BDI) ve Türkiye toplam ihracat ve ithalat rakamları alınmıştır.
Sonuçlar göstermiştir ki Türkiye Dünya deniz ticaretinden etkilenen ve Dünya
deniz ticaretini etkileyen bir ülke konumundadır. 

Anahtar Kelimeler

Baltık Kuru Yük Endeksi, Deniz Ticareti, İhracat, İthalat

WORLD MARITIME TRADE AND TURKISH FOREIGN TRADE RELATIONS: AN ECONOMETRİC ANALYSIS

Öz

Sea, shipping,
sea transportation etc. consepts have not lost their importance throughout the
history of the world and have played important roles in the formation and
development of civilizations. Approximately 75% of the world trade is done by
sea. There are several reasons why maritime transport is still the most preferred
transport alternative. Turkey is also a major maritime country where more than
half of total exports and imports by sea. In the study, world maritime trade
and trade relations with Turkey were investigated. For this purpose,
representing the world maritime trade, the Baltic Dry Index (BDI) and Turkey's
total export and import figures were taken. The results showed that Turkey is a
country affected by world trade and affecting the position of world maritime
trade.

Anahtar Kelimeler

Baltic Dry Index, Maritime Trade, Export, Import

Kaynakça

• Açık, A., & Başer, S. Ö. (2017) The Relatıonshıp Between Freıght Revenues And Vessel Dısposal Decısıons. Ekonomi, Politika & Finans Araştırmaları Dergisi, 2(2), 96-112.
• Açık, A., & Başer, S. Ö. Baltık Kuru Yük Endeksi Etkin Mi?. Journal of Yaşar University, 13(50), 140-149.Angelopoulos, J. (2017). Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade. Maritime Economics & Logistics, 19(1), 126-159.
• Bildirici, M. E., Kayıkçı, F., & Onat, I. Ş. (2015). Baltic Dry Index as a major economic policy indicator: the relationship with economic growth. Procedia-Social and Behavioral Sciences, 210, 416-424.
• Chou, C. C., & Lin, K. S. (2018). A fuzzy neural network combined with technical indicators and its application to Baltic Dry Index forecasting. Journal of Marine Engineering & Technology, 1-10.
• Cihangir, Ç. K. (2018). Küresel Risk Algısının Küresel Ticaret Üzerindeki Etkisi. İşletme Ve Iktisat Calismalari Dergisi, 6(1), 1-10.
• Ghiorghe, B., Gianina, C., & Ioana, S. (2013). Application of autoregressive models for forecasting the Baltic Exchange Dry Index. Universitatii Maritime Constanta. Analele, 14(20), 205.
• Graham, M., Peltomäki, J., & Piljak, V. (2016). Global economic activity as an explicator of emerging market equity returns. Research in International Business and Finance, 36, 424-435.
• Hatemi-j, A. (2012). Asymmetric causality tests with an application. Empirical Economics, 43(1), 447-456.
• Koçak, H. İ. Dünyada Ve Türkiye’de Ekonomik Gelişmeler Ve Deniz Ticaretine Yansımaları. Deniz Ticaret Genel Müdürlüğü Yayınları, Ankara, 2012.
• Lin, F., & Fu, D. (2016). Trade, institution quality and income inequality. World Development, 77, 129-142.
• Lin, F., & Sim, N. C. (2012). Trade, income and the baltic dry index. European Economic Review, 59, 1-18.
• Lin, F., & Sim, N. C. (2014). Baltic Dry Index and the democratic window of opportunity. Journal of Comparative Economics, 42(1), 143-159.
• Papailias, F., Thomakos, D. D., & Liu, J. (2017). The Baltic Dry Index: cyclicalities, forecasting and hedging strategies. Empirical Economics, 52(1), 255-282.
• Ruan, Q., Wang, Y., Lu, X., & Qin, J. (2016). Cross-correlations between Baltic Dry Index and crude oil prices. Physica A: Statistical Mechanics and its Applications, 453, 278-289.
• Şahin, B., Gürgen, S., Ünver, B., & Altın, İ. (2018). Forecasting The Baltic Dry Index By Using An Artificial Neural Network Approach. Turkish Journal Of Electrical Engineering & Computer Sciences, 26(3), 1673-1684.
• Sartorius, K., Sartorius, B., & Zuccollo, D. (2018). Does the Baltic Dry Index predict economic activity in South Africa? A review from 1985 to 2016. South African Journal of Economic and Management Sciences, 21(1), 1-9.
• Şipal, Ö. G. D. Y. Z. (2016). Türkiye’de Ticari Deniz Taşımacılığı Ve Gemi Fiyatlarında Arz-Talep Dengesizliği, Navlun Fiyatlarına Yansıması. IMUCO 2016, 641.
• Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of econometrics, 66(1-2), 225-250.
• Todorut, A. V., Paliu-Popa, L., & Cirnu, D. (2016). Interdependence between iron ore production and maritime transport. Metalurgija, 55(4), 859-861.
• Tsioumas, V., Papadimitriou, S., Smirlis, Y., & Zahran, S. Z. (2017). A Novel Approach to Forecasting the Bulk Freight Market. The Asian Journal of Shipping and Logistics, 33(1), 33-41.
• Yılancı, V., & Bozoklu, Ş. (2014). Türk Sermaye Piyasasında Fiyat ve İşlem Hacmi İlişkisi: Zamanla Değişen Asimetrik Nedensellik Analizi. Ege Academic Review, 14(2).
• Yıldız, B., & Bucak, U. (2018). Determinants of Freight Rates: A Study on the Baltic Dry Index.
• Zeng, Q., & Qu, C. (2014). An approach for Baltic Dry Index analysis based on empirical mode decomposition. Maritime Policy & Management, 41(3), 224-240.
• Zeng, Q., Qu, C., Ng, A. K., & Zhao, X. (2016). A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks. Maritime Economics & Logistics, 18(2), 192-210.
• Zhang, X., Xue, T., & Stanley, H. E. (2019). Comparison of Econometric Models and Artificial Neural Networks Algorithms for the Prediction of Baltic Dry Index. IEEE Access, 7, 1647-1657.