Stochastic public debt projections using the historical variance-covariance matrix approach for EU countries

Stochastic projections are a powerful tool to feature uncertainty in macroeconomic conditions into the analysis of public debt dynamics. They allow simulating a very large number of debt paths, corresponding to as many shock constellations to the non-fiscal determinants of debt evolution (short- and long-term interest rates, growth rate and exchange rate). Furthermore, random shocks are simulated in a way to reflect the size and the correlation of historical shocks. The specific approach for stochastic projections used here, based on the variance-covariance matrix of historical shocks, further allows defining a "central scenario" (for which we use ECFIN's Autumn 2012 forecasts), around which shocks apply. The paper applies this methodology to 24 EU countries over 2013-17. Cross-country differences in the variance of the debt-to-GDP ratio distributions (reflecting differences in historical volatility of macroeconomic conditions) emerge clearly from the simulations. This shows the importance of allowing for a more comprehensive and country-tailored assessment of downward and upward risks to debt dynamics. This stochastic framework also has the distinctive advantage of allowing for an explicit probabilistic assessment of debt projection results. A closer scrutiny of three EU countries in the case with temporary shocks reveals, for instance, that the most likely outcome for IT over 2013-17 is a decreasing path for the debt ratio (though this is projected to be still higher than 116% with a 50% probability in 2017). For ES, simulations show an increasing path over the projection horizon for all shock constellations, with an 80% probability of a debt ratio greater than 100% in 2017. Finally, for HU, we obtain a 60% probability that the debt ratio stabilises or reaches higher values from 2013 onwards, with a 40% probability of a debt ratio greater than 80% in 2017.