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Indicators of economic crises: a data-driven clustering approach

Authors
  • Göbel, Maximilian1
  • Araújo, Tanya1
  • 1 UECE/REM- ISEG, Universidade de Lisboa, R. Miguel Lupi 20, 1249-078, Lisboa, Portugal , Lisboa (Portugal)
Type
Published Article
Journal
Applied Network Science
Publisher
Springer International Publishing
Publication Date
Aug 04, 2020
Volume
5
Issue
1
Identifiers
DOI: 10.1007/s41109-020-00280-4
Source
Springer Nature
Keywords
License
Green

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 self-calibrated 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 defined network topology. We quantitatively evaluate the degree of matching between the data-generated clustering and the desired community-structure.

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