Using pooled information and bootstrap methods to assess debt sustainability in low income countries

Conventional assessments of debt sustainability in low income countries are hampered by poor data and weaknesses in methodology. In particular, the standard International Monetary Fund-World bank debt sustainability framework relies on questionable empirical assumptions: its baseline projections ignore statistical uncertainty, and its stress tests, which are performed as robustness checks, lack a clear economic interpretation and ignore the interdependence between the relevant macroeconomic variables. This paper proposes to alleviate these problems by pooling data from many countries and estimating the shocks and macroeconomic interdependence faced by a generic, low income country. The paper estimates a panel vector autoregression to trace the evolution of the determinants of debt, and performs simulations to calculate statistics on external debt for individual countries. The methodology allows for the value of the determinants of debt to differ across countries in the long run, and for additional heterogeneity through country-specific exogenous variables. Results in this paper suggest that ignoring the uncertainty and interdependence of macroeconomic variables leads to biases in projected debt trajectories, and consequently, the assessment of debt sustainability.