EconPapers    
Economics at your fingertips  
 

Predicting sovereign debt crises: An Early Warning System approach

Mary Dawood, Nicholas Horsewood and Frank Strobel

Journal of Financial Stability, 2017, vol. 28, issue C, 16-28

Abstract: In light of the renewed challenge to construct effective “Early Warning Systems” for sovereign debt crises, we empirically evaluate the predictive power of econometric models developed so far across developed and emerging country regions. We propose a different specification of the crisis variable that allows for the prediction of new crisis onsets as well as duration, and develop a more powerful dynamic-recursive forecasting technique to generate more accurate out-of-sample warning signals of sovereign debt crises. Our results are shown to be more accurate compared to the ones found in the existing literature.

Keywords: Sovereign debt crisis; Early Warning System; Logit; Dynamic signal extraction; Dynamic-recursive forecasting (search for similar items in EconPapers)
JEL-codes: F34 F37 C53 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1572308916301772
Full text for ScienceDirect subscribers only

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:eee:finsta:v:28:y:2017:i:c:p:16-28

Access Statistics for this article

Journal of Financial Stability is currently edited by I. Hasan, W. C. Hunter and G. G. Kaufman

More articles in Journal of Financial Stability from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2019-04-06
Handle: RePEc:eee:finsta:v:28:y:2017:i:c:p:16-28