Recent studies propose different methods for bringing uncertainty into the analysis (Barnhill and Kopits, 2003; IMF, 2002 and 2003; Xu and Ghezzi, 2003; Mendoza and Oviedo 2004 among others). The model by Barnhill and Kopits (2003) and that by Xu and Ghezzi (2003) measure the government’s net worth as an explicitly stochastic process and assess the probability of government default. Mendoza and Oviedo (2004) construct an explicitly stochastic model to explain the apparent inability of governments in emerging markets to borrow as much as governments in industrial countries.

Burnside (2004) reviews these papers and asserts that while there are important lessons to be learned from the proposed methods qualitatively, critical problems with each method suggest no major improvements of the forecasting power of quantitative fiscal sustainability exercises. He argues that only IMF (2002) provides a thorough discussion that can be of use to practitioners, while IMF (2003) formalizes the sensitivity analysis and offers a method for computing confidence intervals around the debt path by estimating a VAR model and using Monte Carlo or bootstrap procedures. However, while the IMF approaches to dealing with uncertainty are easily applied, they too suffer from specific flaws as alternative scenarios draw only loosely on the past distribution of variables that matter in the simulation.

A recent paper by Celasun, Debrun and Ostry (2006) proposes a probabilistic approach to public debt sustainability analysis using “fan charts” which depict the magnitude of uncertainties affecting debt projections. Their simulation algorithm for the path of public debt combines the effects of economic disturbances, the endogenous policy response to these, and the possible shocks arising from the fiscal policy itself. Lack of historical time series data however impedes the application of the VAR technique in IMF (2003) and the one in Celasun, Debrun and Ostry (2006).

We therefore proceed by following the method in IMF (2002). We compliment the analysis with an in-depth discussion of key fiscal risks and constraints to fiscal adjustment. First, we set key parameters at their historical averages for the period 1991-2001. This allows us to illustrate the ambitiousness of the baseline projection relative to historical experience, including, for instance, whether the adjustment envisaged in the baseline projection far exceeds the state’s historical norm. The historical averages, including the impact of the fiscal crisis in the late 1990s, are the outcome of national and subnational policies. Risks inherent in these policies are not completely eliminated, which could exert negative influence on the debt path.

We consider also the impact of an adverse two-standard deviations combined shock to all key parameters, lasting for two consecutive years. Although we undertake scenarios in which one of the variables moves away from its historical average by two standard deviations while the others remain constant, Burnside (2004) considers such scenarios extremely improbable. Highly unlikely are also scenarios in which inflation and interest rates move in opposite directions. Thus, we consider the joint impact of a change in the real interest rate and a negatively correlated change in the primary balance and the real GSDP. We set the real interest rate at its historical average for the period 1991-2001 plus one standard deviation, while the GSDP and the primary balance are at the historical 21