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Indicators of Economic Crises : A Data-Driven Clustering Approach

dc.contributor.authorGöbel, Maximilian
dc.contributor.authorAraújo, Tanya
dc.date.accessioned2020-05-25T16:58:29Z
dc.date.available2020-05-25T16:58:29Z
dc.date.issued2020-05
dc.description.abstractThe 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGöbel, 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 – 2020pt_PT
dc.identifier.issn2184-108X
dc.identifier.urihttp://hdl.handle.net/10400.5/20106
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherISEG - REM - Research in Economics and Mathematicspt_PT
dc.relation.ispartofseriesREM Working paper;nº 0128 – 2020
dc.relation.publisherversionhttps://rem.rc.iseg.ulisboa.pt/wps/pdf/REM_WP_0128_2020.pdfpt_PT
dc.subjectEarly-Warning Modelspt_PT
dc.subjectCrisis Predictionpt_PT
dc.subjectMacroeconomic Dynamicspt_PT
dc.subjectNetwork Analysispt_PT
dc.subjectCommunity Structurept_PT
dc.subjectGreat Recessionpt_PT
dc.subjectClustering Algorithmpt_PT
dc.titleIndicators of Economic Crises : A Data-Driven Clustering Approachpt_PT
dc.typeworking paper
dspace.entity.typePublication
rcaap.rightsopenAccesspt_PT
rcaap.typeworkingPaperpt_PT

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