# **5 Discussion**

The approach taken in this methodology combines several elements in the debt-default literature to not only predict possible default, but to also describe sources of vulnerabilities.

This is where the vulnerability index plays an important role. While looking at several single indicators is useful to understand potential vulnerabilities, a country-specific weighted index summarizes a large set of information. Dimension reduction techniques are well known for picking up signals in a large data group.

A key element of this analysis is a description of what is meant by default probability. There are several definitions of default probability that can be applied. However, here we use only two measures. The first relates to the ability of the government to maintain debt on a stable path. The second relates to investor perceptions regarding default. Monitoring both measures reveal interesting details regarding periods of weakened perceptions over and above the government’s own ability. Once the two default estimates diverge then it points to a panic.

Our methodology attempts to incorporate expert judgment in describing defaults. DSAs provide guidance on key thresholds of risk for several indicator variables. We use these thresholds along with historical data to codify judgment. We note that this judgment is subject to revisions as more information and better analytical tools help to bridge several gaps.

A novel addition to default prediction is to incorporate more structural and theoretical models. We utilize a standard general equilibrium framework to compute debt thresholds. This is different compared to empirical relationships between the size of debt and default to define thresholds. In this example, debt thresholds are determined by the maturity structure, the probability of a panic, and the size of the government. These measures provide insights on whether countries will do well to reduce debts to reduce risks. Two thresholds provide three regions of risk. There is a debt to GDP ratio where the risk of a default is very low. At the same time, there is a debt to GDP ratio where default is very likely or imminent. Finally, there is a region between the no default and default thresholds where countries are vulnerable to exogenous shocks. These thresholds are useful from a policy dialogue perspective to identify sources of risks and policies to reduce those risks.

All of these measures are used jointly to predict defaults. We benefit from advances in the econometric literature and test several specifications. In general, the predictions from the models do a reasonable job in predicting both in and out-of-sample defaults. Interestingly, the count index has the weakest explanatory power in predicting defaults.

There are obvious caveats to this methodology. As with any model, the outputs depend on the quality of the inputs. In the exercise described above, three significant sources of risk are not entirely captured due to lack of data: the realization of both implicit or explicit guarantees, the uncovering of hidden debts, and the risks associated with systemic shocks that spill over to countries. In cases where these data are available, the methodology can be appended to revise the results.

It should also be emphasized that the outputs of the debt vulnerability methodology are intended to supplement and support country risk assessments and not replace them. They indicate rising or abating vulnerabilities and analyze where the vulnerability build-up occurs. Consequently, the outputs in this paper tie in with expert opinion using DSAs and 26