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https://hdl.handle.net/20.500.14279/14675
Τίτλος: | Optimal design of early warning systems for sovereign debt crises | Συγγραφείς: | Fuertes, Ana Maria Kalotychou, Elena |
metadata.dc.contributor.other: | Καλοτύχου, Έλενα | Major Field of Science: | Social Sciences | Field Category: | Economics and Business | Λέξεις-κλειδιά: | Clustering;Loss function;Logit forecast;Forecast combining;Emerging markets;Default prediction;Country risk analysis | Ημερομηνία Έκδοσης: | Ιαν-2007 | Πηγή: | International Journal of Forecasting, 2007, vol. 23, no. 1, pp. 85-100 | Volume: | 23 | Issue: | 1 | Start page: | 85 | End page: | 100 | Περιοδικό: | International Journal of Forecasting | Περίληψη: | 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 |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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