Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/229022 
Autor:innen: 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
ECB Working Paper No. 2408
Verlag: 
European Central Bank (ECB), Frankfurt a. M.
Zusammenfassung: 
This study seeks to answer whether it is possible to design an early warning system framework that can signal the risk of fiscal stress in the near future, and what shape such a system should take. To do so, multiple models based on econometric logit and the random forest models are designed and compared. Using a dataset of 20 annual frequency variables pertaining to 43 advanced and emerging countries during 1992-2018, the results confirm the possibility of obtaining an effective model, which correctly predicts 70-80% of fiscal stress events and tranquil periods. The random forest-based early warning model outperforms logit models. While the random forest model is commonly understood to provide lower interpretability than logit models do, this study employs tools that can be used to provide useful information for understanding what is behind the black-box. These tools can provide information on the most important explanatory variables and on the shape of the relationship between these variables and the outcome classification. Thus, the study contributes to the discussion on the usefulness of machine learning methods in economics.
Schlagwörter: 
early warning system
interpretability of machine learning
predictive performance
JEL: 
C40
C53
H63
G01
Persistent Identifier der Erstveröffentlichung: 
ISBN: 
978-92-899-4051-1
Dokumentart: 
Working Paper

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