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Understanding and predicting sovereign debt rescheduling: a comparison of the areas under receiver operating characteristic curves

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  • Pedro N. Rodriguez

    (Departamento de Estadística e Investigación Operativa II, Universidad Complutense de Madrid, Madrid, Spain)

  • Arnulfo Rodriguez

    (Banco de México, Mexico City, Mexico)

Abstract

This paper extends the existing literature on empirical research in the field of sovereign debt. To the authors' knowledge, only one study in the area of sovereign debt has used a variety of statistical methodologies to test the reliability of their predictions and to compare their performance against one another. However, those comparisons across models have been made in terms of different probability cut-off points and mean squared errors. Moreover, the issue of interpretability has not been addressed in terms of interactions among explanatory variables with their correspondent debt rescheduling threshold level. The areas under the Receiver Operating Characteristic (ROC) curves are used to compare the discrimination power of statistical models. This paper tests logit, MARS, tree-based and neural network models. Analyses of the relative importance of variables and deviance were done. All of the models rank the previous payment history as the most important explanatory variable. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
  • Handle: RePEc:jof:jforec:v:25:y:2006:i:7:p:459-479
    DOI: 10.1002/for.998
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    References listed on IDEAS

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    1. 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.
    2. Galindo, J & Tamayo, P, 2000. "Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications," Computational Economics, Springer;Society for Computational Economics, vol. 15(1-2), pages 107-143, April.
    3. Peter Sephton, 2001. "Forecasting recessions: can we do better on MARS?," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 39-49.
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    Cited by:

    1. Pasiouras, Fotios & Tanna, Sailesh, 2010. "The prediction of bank acquisition targets with discriminant and logit analyses: Methodological issues and empirical evidence," Research in International Business and Finance, Elsevier, vol. 24(1), pages 39-61, January.
    2. Fantazzini, Dean, 2008. "Credit Risk Management," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 12(4), pages 84-137.
    3. Tonatiuh Peña & Serafín Martínez & Bolanle Abudu, 2009. "Bankruptcy Prediction: A Comparison of Some Statistical and Machine Learning Techniques," Working Papers 2009-18, Banco de México.

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