Indicators of Economic Crises: A Data-Driven Clustering Approach

Maximilian Gobel and Tanya Araújo

No 2020/0128, Working Papers REM from ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa

Abstract: The determination of reliable early-warning indicators of economic crises is a hot topic in economic sciences. Pinning down recurring patterns or combinations of macroeconomic indicators is indispensable for adequate policy adjustments to prevent a looming crisis. We investigate the ability of several macroeconomic variables telling crisis countries apart from non-crisis economies. We introduce a selfcalibrated clustering-algorithm, which accounts for both similarity and dissimilarity in macroeconomic fundamentals across countries. Furthermore, imposing a desired community structure, we allow the data to decide by itself, which combination of indicators would have most accurately foreseen the exogeneously de?ned network topology. We quantitatively evaluate the degree of matching between the data-generated clustering and the desired community-structure.

Keywords: Early-Warning Models; Crisis Prediction; Macroeconomic Dynamics; Network Analysis; Community Structure; Great Recession; Clustering Algorithm (search for similar items in EconPapers)
JEL-codes: C38 C52 G01 (search for similar items in EconPapers)
Date: 2020-05
New Economics Papers: this item is included in nep-cmp and nep-net
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://rem.rc.iseg.ulisboa.pt/wps/pdf/REM_WP_0128_2020.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:ise:remwps:wp01282020

Access Statistics for this paper

More papers in Working Papers REM from ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa ISEG - Lisbon School of Economics and Management, REM, R. Miguel Lupi, 20, LISBON, PORTUGAL.
Bibliographic data for series maintained by Sandra Araújo ().