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Predicting Sovereign Debt Crises

41 Pages Posted: 14 Feb 2006  

Paolo Manasse

Università degli Studi di Bologna - Department of Economics; IGIER, Bocconi University; International Monetary Fund (IMF) - Fiscal Affairs Department

Nouriel Roubini

New York University - Leonard N. Stern School of Business - Department of Economics; National Bureau of Economic Research (NBER)

Axel Schimmelpfennig

International Monetary Fund (IMF)

Date Written: November 2003

Abstract

We develop an early-warning model of sovereign debt crises. A country is defined to be in a debt crisis if it is classified as being in default by Standard & Poor's, or if it has access to nonconcessional IMF financing in excess of 100 percent of quota. By means of logit and binary recursive tree analysis, we identify macroeconomic variables reflecting solvency and liquidity factors that predict a debt-crisis episode one year in advance. The logit model predicts 74 percent of all crises entries while sending few false alarms, and the recursive tree 89 percent while sending more false alarms.

Keywords: Early-warning system, sovereign debt crises, sovereign default

JEL Classification: H63, E66, C53

Suggested Citation

Manasse, Paolo and Roubini, Nouriel and Schimmelpfennig, Axel, Predicting Sovereign Debt Crises (November 2003). IMF Working Paper, Vol. , pp. 1-41, 2003. Available at SSRN: https://ssrn.com/abstract=880911

Paolo Manasse (Contact Author)

Università degli Studi di Bologna - Department of Economics ( email )

Via Strada Maggiore, 45
I-40125 Bologna
Italy
+39 05 1209 2613 (Phone)

IGIER, Bocconi University

Via Sarfatti 25
20136 Milan, MI 20136
Italy
+39 02 5836 3326 (Phone)

International Monetary Fund (IMF) - Fiscal Affairs Department ( email )

700 19th Street, NW
Washington, DC 20431
United States

Nouriel Roubini

New York University - Leonard N. Stern School of Business - Department of Economics ( email )

269 Mercer Street
New York, NY 10003
United States
212-998-0886 (Phone)
212-995-4218 (Fax)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Axel Schimmelpfennig

International Monetary Fund (IMF) ( email )

700 19th Street NW
Washington, DC 20431
United States

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