Predicting Sovereign Debt Crises

Axel Schimmelpfennig, Nouriel Roubini and Paolo Manasse

No 2003/221, IMF Working Papers from International Monetary Fund

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: WP; debt crisis; crisis entry; crises episode; debt-crisis indicator; currency crisis; Early-warning system; sovereign debt crises; sovereign default; debt-crisis entry; debt-crisis episode; crisis probability; debt-crisis event; crisis definition; debt crises episode; debt-servicing difficulties; probability rise; debt-servicing pressure; crisis exit; debt-servicing obligation; Debt default; Early warning systems; Africa (search for similar items in EconPapers)
Pages: 41
Date: 2003-11-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (222)

Downloads: (external link)
http://www.imf.org/external/pubs/cat/longres.aspx?sk=16951 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:imf:imfwpa:2003/221

Ordering information: This working paper can be ordered from
http://www.imf.org/external/pubs/pubs/ord_info.htm

Access Statistics for this paper

More papers in IMF Working Papers from International Monetary Fund International Monetary Fund, Washington, DC USA. Contact information at EDIRC.
Bibliographic data for series maintained by Akshay Modi ().