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Indicators of Economic Crises : A Data-Driven Clustering Approach
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Autores
Orientador(es)
Resumo(s)
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 defined network topology. We quantitatively evaluate the degree of matching between the data-generated clustering and the desired community-structure.
Descrição
Palavras-chave
Early-Warning Models Crisis Prediction Macroeconomic Dynamics Network Analysis Community Structure Great Recession Clustering Algorithm
Contexto Educativo
Citação
Göbel, Maximilian e Tanya Araújo (2020). "Indicators of Economic Crises : A Data-Driven Clustering Approach". Instituto Superior de Economia e Gestão – REM Working paper nº 0128 – 2020
Editora
ISEG - REM - Research in Economics and Mathematics