Optimal design of early warning systems for sovereign debt crises
File(s)
Journal
International Journal of Forecasting
Date Issued
January 2007
Author(s)
Abstract
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 naïve predictions.