Bilal SOLAK, Nevzat TETİK, Mehmet KARAHANLI

Yapay Sinir Ağları Metodu ile Yunanistan Krizini Etkileyen Major Faktörlerin Belirlenmesi

Anahtar Kelimeler:

Yunanistan Ekonomik Krizi, Yapay Sinir Ağları Modeli, İşsizlik

As globalization spreads worldwide, the interaction between economies makes progress rapidly. This progressive interaction has both negative and positive side effects. For instance, a crisis occurred in one country can easily turn into a global recession. Lately, the crisis in Greek economy has affected world economy in a very short period. In this study factors, that would have been reasons of the Greece economic crisis, were analyzed by Artificial Neural Networks method. The result of the study shows that unemployment was the most important factor of the crisis

Keywords:

Greece Economic Crisis, Artificial Neural Networks Method, Unemployment,

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Ahmad, H.A. and Mariano, M. (2006). Comparison of Forecasting Methodologies Using Egg Price as a Test Case. Poultry Science, 85, ss:798807.
Alkin, E., Yıldırım, K. and Özer, M. (2003), İktisada Giriş (edt. Işıklar İ.), Eskişehir: Anadolu Üniversitesi Yayınları
Altunısık, R., Coskun, R., Bayraktaroglu, S.and Yıldırım, E. (2005) Sosyal Bilimlerde Araştırma Metotları, SPSS Uygulamalı, 4 Baskı, Adapazari, Sakarya Yayıncılık.
Alagöz, M., Işık, N. ve Delice, G. (2006), Finansal Krizler İçin Erken Uyarı Sinyali Olarak Cari Hesap Göstergeleri, Ekonomik Kriz Öncesi Erken Uyarı Sistemleri Makale Derlemesi, Editörler: H. Seyidoğlu ve R. Yıldız, İstanbul:Arıkan Basım yayım Dağıtım Ltd. Şti., ss. 347- 374
Gürkan, Ö. (1999). İktisada Giriş, (3. Baskı), Ankara: Attila Kitabevi IMF (International Monetary Fund) (2010), Country Report, Greece: Staff Report on Request for Stand-By Agreement, No:10/110
Karahan, M. (2011). İstatistiksel Tahmin Yöntemleri: Yapay Sinir Ağları Metodu ile Ürün Talep Tahmini Uygulaması, Doktora Tezi, Selçuk Üniversitesi Sosyal Bilimler Enstitüsü, Konya.
Karahan, M. ve Tetik, N. (2012). The Determination of the Effect Level on Employee Performance of TQM Practices with Artificial Neural Networks: A Case Study on Manufacturing Industry Enterprises in Turkey International Journal of Business and Social Science, 3(7), ss:133-142.
Kartalopoulos, S. V (1996). Understanding Neural Networks and Fuzzy Logic. Basic Consepts and Applications. New York: IEEE Press.
Köse, Y. Karabacak, H. (2011),Yunanistan Ekonomik Krizi: Nedenleri, Etkileri ve Alınan Tedbirlere İlişkin Bir Değerlendirme, Maliye Dergisi, Sayı: 160, Ocak-Haziran 2011, ss: 290-306
Makridakis, S. Wheelwright, S. C. and Hyndman, R. J. (1998). Forecasting Methods and Applications (Third Edition). New York: John Wiley & Sons Inc.
Odyakmaz, Necmi (1997). Groups Correlation Method Based Analysis of foreign trade with the item. Republic of Turkey Prime Ministry Undersecretariat of Foreign Trade, http://www.dtm.gov.tr/ ead/DTDERGİ/nisan97/5.htm.
Oltheten, E., Pinteris, G. ve Sougiannis, T. (2003), Greece in the European Union: Policy Lessons from Two Decades of Membership, The Quarterly Review of Economics and Finance, 43, 774-806.
Sahoo, G.B., Schladow, S.G. and Reuter, J.E. (2009). Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models. Journal of Hydrology, 378, ss:325342.
Sezgin, F. Özdamar, E. Ö. (2011). XI. Üretim Araştırmaları Sempozyumu, Bildiriler Kitapçığı, 23-24 Haziran 2011, ss: 157-167.
SGP (2010), Update of the Hellenic Stability and Growth Programme (Including an Updated Reform Programme), Ministry of Finance, Athens
http://www.tradingeconomics.com/greece