Leading indicators of fiscal distress: evidence from extreme bounds analysis

ABSTRACT

Early warning systems (EWSs) are widely used to assess a country’s vulnerability to fiscal distress. A fiscal distress episode is identified as a period when government experiences extreme funding difficulties. Most EWSs employ a specific set of only fiscal leading indicators predetermined by the researchers, which casts doubt on their robustness. We revisit this issue using extreme bounds analysis, which allows identifying robust leading indicators of fiscal distress from a large set. A robust leading indicator’s effect does not strongly depend on the model specification. Consistent with the theoretical predictions of latest generation crisis models, we find that both fiscal and non-fiscal leading indicators are robust. In addition, we find that a fiscal vulnerability indicator based on fiscal and non-fiscal leading indicators offers a 29% gain in predictive power compared to a traditional one based only on fiscal leading indicators. This suggests that both fiscal and non-fiscal leading indicators should be taken into account when assessing country’s vulnerability to fiscal distress.

KEYWORDS:

• Fiscal distress
• extreme bounds analysis
• early warning systems
• vulnerability index

JEL CLASSIFICATION:

• H63
• E62
• E65
• E66
• F47

Acknowledgements

The authors thank Bernardin Akitoby, Suman Basu, Julio Escolano, Greetje Everaert, Davide Furceri, Vitor Gaspar, Jun Kim, Helmut Lütkepohl, Paulo Medas, Marialuz Moreno Badia, Dieter Nautz, Manrique Saenz, Julian Schreiber, Abdelhak Senhadji and seminar participants at the IMF’s Fiscal Affairs Department, the Freie Universität Empirical Macroeconomics Workshop and the 10th International Conference on Computational and Financial Econometrics (2016) for useful comments and suggestions. Jonathan Lee and Juan Farah Yacoub provided excellent research assistance. Macarena Torres Girao provided excellent editorial assistance. The usual disclaimer applies.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ The data set is available at: http://www.reinhartandrogoff.com/data/.

² The results do not change qualitatively when using a forecasting horizon of 2 years.

³ Our results remain qualitatively unchanged when the sample is restricted to the period 1970–2010 as in Baldacci et al. (2011).

⁴ Unfortunately, we could not replicate the analysis for just the advanced economies due to the limited number of fiscal distress events.

⁵ We have also tried a fixed effects logit specification and the results remain similar. The main drawback of the fixed effect model is that it drops countries that have never experienced a fiscal distress (see Bussière 2013 for a discussion).

⁶ The index does not include primary balance gap variable (because of low numbers of observations), the foreign exchange debt ratio (because the series end in 2012) and the FX reserves to GDP ratio (because we already control for the FX reserves growth variable).

⁷ As an example, consider the current account balance (% of GDP). Its 25th quantile is −0.0998 and its 75th quantile is 0.4465. The coefficient, as given in Table 4, is ${\hat{\beta }}_i=-0.0943$. This gives a log odds ratio of (0.4464 + 0.0998)(−0.0943) = −0.0515 as shown in Figure 3. This means that when current account balance (% of GDP) moves from its 25th to its 75th quantile, holding the other variables constant, the log odds ratio will change by −0.0515.

⁸ Some studies also apply relatively less standard techniques, such as binary recursive trees, artificial neutral networks and Markov switching models.

⁹ In the meta-analysis literature, this phenomenon is often referred to as ‘publication bias’.