Dynamic nonparametric clustering of multivariate panel data

Igor Custodio Joao, Andre Lucas, Julia Schaumburg and Bernd Schwaab

No 2780, Working Paper Series from European Central Bank

Abstract: We introduce a new dynamic clustering method for multivariate panel data char-acterized by time-variation in cluster locations and shapes, cluster compositions, and, possibly, the number of clusters. To avoid overly frequent cluster switching (flickering), we extend standard cross-sectional clustering techniques with a penalty that shrinks observations towards the current center of their previous cluster as-signment. This links consecutive cross-sections in the panel together, substantially reduces flickering, and enhances the economic interpretability of the outcome. We choose the shrinkage parameter in a data-driven way and study its misclassification properties theoretically as well as in several challenging simulation settings. The method is illustrated using a multivariate panel of four accounting ratios for 28 large European insurance firms between 2010 and 2020. JEL Classification: C33, C38, G22

Keywords: cluster membership persistence; dynamic clustering; insurance industry; shrinkage; sil-houette index (search for similar items in EconPapers)
Date: 2023-02
New Economics Papers: this item is included in nep-ecm
Note: 955417
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Dynamic Nonparametric Clustering of Multivariate Panel Data* (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20232780

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