Analysis of international debt problem using artificial neural networks and statistical methods

YAZICI B., MEMMEDLİ M., ASLANARGUN A., ASMA Ş.

NEURAL COMPUTING & APPLICATIONS, vol.19, no.8, pp.1207-1216, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 19 Issue: 8
  • Publication Date: 2010
  • Doi Number: 10.1007/s00521-010-0422-4
  • Journal Name: NEURAL COMPUTING & APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.1207-1216
  • Keywords: Artificial neural network, Backpropagation algorithm, Conjugate gradient method, Quasi-Newton method, Logistic and probit regression, Rescheduling and non-rescheduling of the international debts, SERVICING CAPACITY, MOMENTUM, DESCENT, CRISIS
  • Anadolu University Affiliated: Yes

Abstract

It is known from the scientific researches that artificial neural networks are alternatives of statistical methods such as regression analysis and classification in recent years. Since multi-layer backpropagation neural network models are nonlinear, it is expected that the neural network models should make better classifications and predictions. The studies on this subject support that idea. In this study, a macro-economic problem on rescheduling or non-rescheduling of the countries' international debts is taken into account. Among the statistical methods, logistic and probit regression, and the different neural network backpropagation algorithms are applied and comparisons are made. Evaluations and suggestions are made depending on the results and different neural network architecture.