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

• Published in: Neural computing & applications Vol. 19; no. 8; pp. 1207 - 1216
• Main Authors: Yazici, Berna, Memmedli, Memmedaga, Aslanargun, Atilla, Asma, Senay
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.11.2010; Springer
• Subjects: Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Exact sciences and technology; Image Processing and Computer Vision; Inference from stochastic processes; time series analysis; Learning and adaptive systems; Linear inference, regression; Mathematics; Multivariate analysis; Original Article; Probability and statistics; Probability and Statistics in Computer Science; Sciences and techniques of general use; Statistics; Conjugate gradient method; Logistic and probit regression; Backpropagation algorithm; Quasi-Newton method; Artificial neural network; Rescheduling and non-rescheduling of the international debts; Backpropagation; Evaluation; Neural computation; Statistical analysis; Prediction; Regression analysis; Neural network; Economic sciences; Neural net architecture; Statistical method; Statistical regression; Logistic regression; Analysis method; Classification; Multilayer network
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-010-0422-4

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.