Optimal Design of an Early Warning Systems for Sovereign Debt Crises
International Journal of Forecasting 23, 85-100
Posted: 29 Dec 2004 Last revised: 11 Sep 2019
Date Written: December 10, 2004
This paper tackles the design of an optimal early warning system (EWS) for sovereign default from two distinct angles: the choice of the econometric methodology and the evaluation of the EWS itself. It compares K-means clustering of macrodata, a logit regression for macrodata, a logit regression for credit ratings and the combined forecasts from all three methods. The optimal choice of forecast method is shown to depend on the desired trade-off between missed defaults and false alarms. Hence, it is crucial to account for the decision-maker's preferences which are characterized through a loss function and risk aversion parameter. Recursive forecast combining generally yields a better balance of Type I and Type II errors than any of the individual forecasting methods and outperforms the naive predictions.
Keywords: Debt crises, K-means clustering, logistic regression, bank internal ratings, loss function, forecast combination
JEL Classification: C15, C22, C52
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