Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/14675
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Fuertes, Ana Maria | - |
dc.contributor.author | Kalotychou, Elena | - |
dc.contributor.other | Καλοτύχου, Έλενα | - |
dc.date.accessioned | 2019-07-23T07:34:33Z | - |
dc.date.available | 2019-07-23T07:34:33Z | - |
dc.date.issued | 2007-01 | - |
dc.identifier.citation | International Journal of Forecasting, 2007, vol. 23, no. 1, pp. 85-100 | en_US |
dc.identifier.issn | 01692070 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/14675 | - |
dc.description.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. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | International Journal of Forecasting | en_US |
dc.rights | © Elsevier | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Clustering | en_US |
dc.subject | Loss function | en_US |
dc.subject | Logit forecast | en_US |
dc.subject | Forecast combining | en_US |
dc.subject | Emerging markets | en_US |
dc.subject | Default prediction | en_US |
dc.subject | Country risk analysis | en_US |
dc.title | Optimal design of early warning systems for sovereign debt crises | en_US |
dc.type | Article | en_US |
dc.collaboration | City University London | en_US |
dc.subject.category | Economics and Business | en_US |
dc.journals | Subscription | en_US |
dc.country | United Kingdom | en_US |
dc.subject.field | Social Sciences | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1016/j.ijforecast.2006.07.001 | en_US |
dc.identifier.scopus | 2-s2.0-33847039550 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/33847039550 | - |
dc.relation.issue | 1 | en_US |
dc.relation.volume | 23 | en_US |
cut.common.academicyear | 2006-2007 | en_US |
dc.identifier.spage | 85 | en_US |
dc.identifier.epage | 100 | en_US |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.grantfulltext | none | - |
crisitem.author.dept | Department of Finance, Accounting and Management Science | - |
crisitem.author.faculty | Faculty of Tourism Management, Hospitality and Entrepreneurship | - |
crisitem.author.orcid | 0000-0003-2824-0383 | - |
crisitem.author.parentorg | Faculty of Management and Economics | - |
crisitem.journal.journalissn | 0169-2070 | - |
crisitem.journal.publisher | Elsevier | - |
Appears in Collections: | Άρθρα/Articles |
CORE Recommender
SCOPUSTM
Citations
47
checked on Mar 14, 2024
WEB OF SCIENCETM
Citations
42
Last Week
0
0
Last month
0
0
checked on Oct 29, 2023
Page view(s) 50
343
Last Week
0
0
Last month
6
6
checked on Aug 30, 2024
Google ScholarTM
Check
Altmetric
This item is licensed under a Creative Commons License