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Predicting LDC Debt Rescheduling: Performance Evaluation of OLS, Logit, and Neural Network Models

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  • Barney, Douglas K
  • Alse, Janardhanan A

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 by John Wiley & Sons, Ltd.

Suggested Citation

  • Barney, Douglas K & Alse, Janardhanan A, 2001. "Predicting LDC Debt Rescheduling: Performance Evaluation of OLS, Logit, and Neural Network Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(8), pages 603-615, December.
    • Handle: RePEc:jof:jforec:v:20:y:2001:i:8:p:603-15
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    Cited by:

    1. Pedro N. Rodriguez & Arnulfo Rodriguez, 2006. "Understanding and predicting sovereign debt rescheduling: a comparison of the areas under receiver operating characteristic curves," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(7), pages 459-479.
    2. Duane Rockerbie & Stephen Easton, 2003. "Information as a Substitute for Bailouts in Sovereign Debt Markets," International Finance 0303003, University Library of Munich, Germany.

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