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

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  • Hevia, Constantino

Abstract

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.

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Bibliographic Info

Paper provided by The World Bank in its series Policy Research Working Paper Series with number 5978.

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Date of creation: 01 Feb 2012
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Handle: RePEc:wbk:wbrwps:5978

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Keywords: Economic Theory&Research; External Debt; Debt Markets; Emerging Markets; Bankruptcy and Resolution of Financial Distress;

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  1. Everaert, Gerdie & Pozzi, Lorenzo, 2007. "Bootstrap-based bias correction for dynamic panels," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 31(4), pages 1160-1184, April.
  2. Pesaran, M. H. & Shin, Y., 1997. "Generalised Impulse Response Analysis in Linear Multivariate Models," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge 9710, Faculty of Economics, University of Cambridge.
  3. Thomas Fomby & Yuki Ikeda & Norman V. Loayza, 2013. "The Growth Aftermath Of Natural Disasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 412-434, 04.
  4. Hisashi Tanizaki & Shigeyuki Hamori & Yoichi Matsubayashi, 2006. "On least-squares bias in the AR(p) models: Bias correction using the bootstrap methods," Statistical Papers, Springer, Springer, vol. 47(1), pages 109-124, January.
  5. Nathaniel Frank & Eduardo Ley, 2009. "On the Probabilistic Approach to Fiscal Sustainability: Structural Breaks and Non-Normality," IMF Staff Papers, Palgrave Macmillan, vol. 56(4), pages 742-757, November.
  6. Roberto Rigobon & Marcio Garcia, 2004. "A Risk Management Approach to Emerging Market’s Sovereign Debt Sustainability with an application to Brazilian data," Econometric Society 2004 Latin American Meetings, Econometric Society 24, Econometric Society.
  7. Oya Celasun & Xavier Debrun & Jonathan David Ostry, 2006. "Primary Surplus Behavior and Risks to Fiscal Sustainability in Emerging Market Countries," IMF Working Papers 06/67, International Monetary Fund.
  8. Broda, Christian, 2004. "Terms of trade and exchange rate regimes in developing countries," Journal of International Economics, Elsevier, Elsevier, vol. 63(1), pages 31-58, May.
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