The results in Table 4 indicate that BMA methods improve on the predictive ability of single models. Model average predictions do particularly well at improving the share of correct alarm signals, at the cost of a small reduction of the share of correctly predicted quiet times. In this sense, our results indicate that methods that make use of quantifications of model uncertainty to aggregate predictions of single models should be added to the instruments used by policymakers to anticipate and measure potential sovereign default risks. It should be noticed that the improvement is relatively modest in quantitative terms with respect to the best model. Partly, this can be explained by the fact that the posterior mass over the model space is very concentrated on very few models, a phenomenon recently dubbed “the supermodel effect” which is related to the priors used in the analysis (see Feldkircher and Zeugner, 2009). The use of a hyperprior structure over parameters should improve mixing among models, but would increase the computational burden of our exercise enormously.

# **5. Conclusions**

Using a database spanning 25 years for 46 EMCs, we use BMA techniques to determine the set of robust determinants of debt default. A first look at the data indicates that countries with different levels of indebtedness have also different characteristics. On average, countries with external debt below 50 percent of GNI (roughly the median of this variable in our sample), grow more, have lower inflation, and achieve primary surpluses compared to countries with a higher level of external debt. They are also less open, and therefore less exposed to shock from external demand, have a lower current account deficit, but have a higher level of reserves than more indebted countries.

For the entire sample, the probability of default is only robustly associated with the level of indebtedness of a country. However, for countries with debt‐to‐GNI ratio below 50 percent, their quality of policies and institutions become also relevant. In countries with external debt above 50 percent of GNI, the institutional quality is not relevant while inflation, together with indebtedness, is positively associated with a higher probability of debt default. The importance of the institutional settings and quality of policies fades away for countries with higher level of debt.

Variables representing debt costs and rollover risk do not appear robust as predictors of debt default. On the one hand, this result confirms the view in the literature that only few macroeconomic variables and variable of institutional quality are necessary to predict defaults (Kraay and Nehru, 2006). On the other hand, it implies that more analysis is necessary to determine the importance of debt management to decrease the probability of default.

We also show that model averaging improves the out‐of‐sample predictive ability for debt crises and that such techniques should become part of the set of instruments used by policymakers to assess the degree of sovereign default risk and obtain informational gains on which to base their economic policy measures. Further improvements may be obtained if a fully Bayesian approach is used to obtain model‐averaged results, using the results by Albert and Chib (1993) in the framework of BMA. This improvement is straightforward from an analytical point of view, but may be computationally costly.

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