Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/14675
Title: Optimal design of early warning systems for sovereign debt crises
Authors: Fuertes, Ana Maria 
Kalotychou, Elena 
metadata.dc.contributor.other: Καλοτύχου, Έλενα
Major Field of Science: Social Sciences
Field Category: Economics and Business
Keywords: Clustering;Loss function;Logit forecast;Forecast combining;Emerging markets;Default prediction;Country risk analysis
Issue Date: Jan-2007
Source: International Journal of Forecasting, 2007, vol. 23, no. 1, pp. 85-100
Volume: 23
Issue: 1
Start page: 85
End page: 100
Journal: International Journal of Forecasting 
Abstract: This paper tackles the design of an optimal early warning system (EWS) for sovereign default from two distinct angles: the choice of the econometric methodology and the evaluation of the EWS itself. It compares K-means clustering of macrodata, a logit regression for macrodata, a logit regression for credit ratings, and the combined forecasts from all three methods. The optimal choice of forecast method is shown to depend on the desired trade-off between missed defaults and false alarms. Hence, it is crucial to account for the decision-maker's preferences which are characterized through a loss function and risk-aversion parameter. Recursive forecast combining generally yields a better balance of type I and type II errors than any of the individual forecasting methods, and outperforms the naïve predictions.
URI: https://hdl.handle.net/20.500.14279/14675
ISSN: 01692070
DOI: 10.1016/j.ijforecast.2006.07.001
Rights: © Elsevier
Type: Article
Affiliation : City University London 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

CORE Recommender
Sorry the service is unavailable at the moment. Please try again later.
Show full item record

SCOPUSTM   
Citations

47
checked on Mar 14, 2024

WEB OF SCIENCETM
Citations

42
Last Week
0
Last month
0
checked on Oct 29, 2023

Page view(s)

387
Last Week
7
Last month
1
checked on Feb 22, 2025

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons