Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/278356 
Erscheinungsjahr: 
2023
Schriftenreihe/Nr.: 
ECB Working Paper No. 2780
Verlag: 
European Central Bank (ECB), Frankfurt a. M.
Zusammenfassung: 
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.
Schlagwörter: 
dynamic clustering
shrinkage
cluster membership persistence
silhouette index
insurance industry
JEL: 
C33
C38
G22
Persistent Identifier der Erstveröffentlichung: 
ISBN: 
978-92-899-5522-5
Dokumentart: 
Working Paper

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