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Indicators of Economic Crises: A Data-Driven Clustering Approach

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  • Maximilian Gobel
  • Tanya Araújo

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.

Suggested Citation

  • Maximilian Gobel & Tanya Araújo, 2020. "Indicators of Economic Crises: A Data-Driven Clustering Approach," Working Papers REM 2020/0128, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    • Handle: RePEc:ise:remwps:wp01282020
    as

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    More about this item

    Keywords

    Early-Warning Models; Crisis Prediction; Macroeconomic Dynamics; Network Analysis; Community Structure; Great Recession; Clustering Algorithm;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G01 - Financial Economics - - General - - - Financial Crises
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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