Random forest versus logit models: which offers better early warning of fiscal stress?

Barbara Jarmulska

No 2408, Working Paper Series from European Central Bank

Abstract: 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. JEL Classification: C40, C53, H63, G01

Keywords: early warning system; interpretability of machine learning; predictive performance (search for similar items in EconPapers)
Date: 2020-05
New Economics Papers: this item is included in nep-big and nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2408~aa6b05aed7.en.pdf (application/pdf)

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:ecb:ecbwps:20202408

Access Statistics for this paper

More papers in Working Paper Series from European Central Bank 60640 Frankfurt am Main, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Official Publications ().