✦ LIBER ✦

Predicting LDC debt rescheduling: performance evaluation of OLS, logit, and neural network models

✍ Scribed by Douglas K. Barney; Janardhanan A. Alse

Publisher: John Wiley and Sons
Year: 2001
Tongue: English
Weight: 150 KB
Volume: 20
Category: Article
ISSN: 0277-6693
DOI: 10.1002/for.802

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✦ Synopsis

Abstract

Empirical studies in the area of sovereign debt have used statistical models singularly to predict the probability of debt rescheduling. Unfortunately, researchers have made few efforts to test the reliability of these model predictions or to identify a superior prediction model among competing models. This paper tested neural network, OLS, and logit models' predictive abilities regarding debt rescheduling of less developed countries (LDC). All models predicted well out‐of‐sample. The results demonstrated a consistent performance of all models, indicating that researchers and practitioners can rely on neural networks or on the traditional statistical models to give useful predictions. Copyright © 2001 John Wiley & Sons, Ltd.

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