TÜRKİYE’DEKİ KONTEYNER LİMANLARININ ÇIKTISINA ÖNCÜ BİR GÖSTERGE OLARAK ENDÜSTRİYEL ÜRETİM

Yıl 2019, Cilt: 11 Sayı: 1, 37 - 56, 13.09.2019

Abdullah Açık , Bayram Bilge Sağlam Burhan Kayıran

https://doi.org/10.18613/deudfd.614828

Cited By: 1

Öz

Bu çalışmanın amacı, limanların
gelecek planlamaları için faydalı sonuçlar elde etmek için Türk limanlarındaki
konteyner trafiği ile Türkiye’nin endüstriyel üretimi arasındaki nedensellik
ilişkisini doğrusal olmayan yapıları ve muhtemel gecikmeli etkileri göz önünde
bulundurarak tespit etmektir. Bu amaç doğrultusunda Diks ve Panchenko (2006)
tarafından önerilen doğrusal olmayan nedensellik testi kullanılmaktadır. Veri
seti Ocak 2005 ve Nisan 2019 dönemleri arasını kapsayan aylık bazda 172
gözlemden oluşmaktadır. Araştırmaya konu olan değişkenlerdeki doğrusal olmayan
yapı göz önünde bulundurularak yapılan analizler sonucunda elde edilen bulgulara
göre, endüstriyel üretim endeksinden liman çıktı hacimlerine tek yönlü anlamlı
nedensellik ilişkisi olduğu ve 3 dönem (ay) boyunca etkisini sürdürdüğü tespit
edilmiştir. Talep seviyesine göre gelecek üretim planlamalarındaki değişimlerin
limanlara yansıması birkaç dönem sürebildiği için, bu durumun Türk
üreticilerinin ithal ara mallarını üretim faaliyetlerinde yoğun olarak
kullanmaları nedeniyle oluştuğu düşünülebilir. Bu sonuçların hem limanlara, hem
de liman kullanıcıları ve politika belirleyicilere strateji geliştirme ve
planlama konularında önemli katkılar sunacağı umulmaktadır. 

Anahtar Kelimeler

Gecikmeli nedensellik, Türk limanları, Endüstriyel Üretim Endeksi

INDUSTRIAL PRODUCTION AS A LEADING INDICATOR FOR CONTAINER PORT THROUGHPUT IN TURKEY

Öz

The purpose of this study is to determine the causal
relationship between container traffic in Turkish ports and industrial
production of Turkey considering the possible nonlinear structures and lagged
impacts in order to generate results which are likely to be useful for the
future planning of the ports. In accordance with this purpose, the non-linear
test proposed by Diks and Panchenko (2006) has been
used. The dataset consists of 172 monthly observations and covers the period
between January 2005 and April 2019. According to the results obtained by
considering the nonlinear structures, there is a significant unidirectional causality
relationship from industrial production index to port throughputs and the
impact continues during 3 periods (months). This situation
can be thought to be caused by the intensive use of imported intermediate goods
by Turkish producers. According to the demand level, it may take several
periods for the changes in the future production planning to be reflected in
the ports. These results are hoped to provide significant contributions both to
ports, port users and policy makers in terms of strategy development and
planning. 

Anahtar Kelimeler

Lagged causality, Turkish Ports, Industrial Production Index

Kaynakça

• Açık, A. and Sağlam, B.B. (2018). Recursive data envelopment analysis in port efficiency: an application on Turkish ports. In: Proceedings of 17th Internationally Participated Business Congress. İzmir, Turkey
• Adıgüzel, U., Bayat, T., Kayhan, S. and Nazlıoğlu, Ş. (2013). Oil prices and exchange rates in Brazil, India and Turkey: Time and frequency domain causality analysis. Siyaset, Ekonomi ve Yönetim Araştırmaları Dergisi, 1(1), 49-73.
• Ajmi, A. N., El Montasser, G. and Nguyen, D. K. (2013). Testing the relationships between energy consumption and income in G7 countries with nonlinear causality tests. Economic Modelling, 35(2013), 126-133.
• Ateş, A. and Esmer, S. (2014). Farklı yöntemler ile Türk konteyner limanlarının verimliliği. Verimlilik Dergisi, (1), 61-76.
• Baek, E. and Brock, W. (1992). A general test for nonlinear Granger causality: bivariate Model. Working Paper. Iowa State University and University of Wisconsin-Madison.
• Bal, D. P. and Rath, B. N. (2015). Nonlinear causality between crude oil price and exchange rate: A comparative study of China and India. Energy Economics, 51(2015), 149-156.
• Balcilar, M., Ozdemir, Z. A. and Cakan, E. (2011). On the nonlinear causality between inflation and inflation uncertainty in the G3 countries. Journal of Applied Economics, 14(2), 269-296.
• Bildirici, M. E. and Turkmen, C. (2015). Nonlinear causality between oil and precious metals. Resources Policy, 46(2), 202-211.
• Brock, W. (1991). Causality, Chaos, Explanation and Prediction in Economics and Finance. Casti, J., Karlqvist, A. (Eds.), . In: Beyond Belief: Randomness, Prediction and Explanation in Science. Boca Raton, Fla: CRC Press
• Chiou-Wei, S. Z., Chen, C. F. and Zhu, Z. (2008). Economic growth and energy consumption revisited—Evidence from linear and nonlinear Granger causality. Energy Economics, 30(6), 3063-3076.
• Chou, C. C., Chu, C. W. and Liang, G. S. (2008). A modified regression model for forecasting the volumes of Taiwan’s import containers. Mathematical and Computer Modelling, 47(9-10), 797-807.
• Dickey, D. A. and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427-431.
• Diks, C. and Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669.
• Diks, C. and Panchenko, V., (2005). A note on the Hiemstra-Jones test for Granger non-causality, Studies in Nonlinear Dynamics and Econometrics. 9(2),1-7.
• Dura, Y. C., Beser, M. K. and Acaroglu, H. (2017). Econometric analysis of Turkey's export-led growth. Ege Akademik Bakış, 17(2), 295-310.
• Gosasang, V., Yip, T. L. and Chandraprakaikul, W. (2018). Long-term container throughput forecast and equipment planning: The case of Bangkok Port. Maritime Business Review, 3(1), 53-69.
• Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society,37(3), 424-438.
• Güner, S. (2015). Investigating infrastructure, superstructure, operating and financial efficiency in the management of Turkish seaports using data envelopment analysis. Transport Policy, 40(2015), 36-48.
• Hiemstra, C. and Jones, J. D. (1994). Testing for linear and nonlinear Granger causality in the stock price‐volume relation. The Journal of Finance, 49(5), 1639-1664.
• Korkmaz, O. (2012). Türkiye'de gemi taşımacılığının bazı ekonomik göstergelere etkisi. Business and Economics Research Journal, 3(2), 97-109.
• Kumar, S. (2017). On the nonlinear relation between crude oil and gold. Resources Policy, 51(2017), 219-224.
• Lättilä, L. and Hilmola, O. P. (2012). Forecasting long-term demand of largest Finnish sea ports. International Journal of Applied Management Science, 4(1), 52-79.
• Tsai, F. M. and Huang, L. J. (2017). Using artificial neural networks to predict container flows between the major ports of Asia. International Journal of Production Research, 55(17), 5001-5010.
• Tunalı, H. and Akarçay, N. (2018). Deniz taşımacılığı ile sanayi üretimi ilişkisinin analizi: Türkiye örneği. İktisadi İdari ve Siyasal Araştırmalar Dergisi, 3(6), 111-122.
• Vitsounis, T., Paflioti, P. and Tsamourgelis, I. (2014). Determinants of container ports throughput convergence. A business cycle synchronicity analysis. International Journal of Transport Economics, 41(2),201-230
• Yu, L., Li, J., Tang, L. and Wang, S. (2015). Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach. Energy Economics, 51(2015), 300-311.
• Internet References:
• Eğilmez, M. (2012). Sanayi Üretimi ve Kapasite Kullanımı Nasıl Ölçülür. Kendime Yazılar, http://www.mahfiegilmez.com/2012/03/sanayi-uretimi-ve-kapasite-kullanm-nasl.html, Access Date: 20.03.2019.
• TurkStat (2019). Industrial Production Index, https://biruni.tuik.gov.tr/medas/?kn=67&locale=tr, Access Date: 20.03.2019.
• UDHB (2019). Container Statistics, https://atlantis.udhb.gov.tr/istatistik/istatistik_yuk.aspx, Access Date: 20.03.2019.