Dynamic Nonparametric Clustering of Multivariate Panel Data*

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

Journal of Financial Econometrics, 2024, vol. 22, issue 2, 335-374

Abstract: We introduce a new dynamic clustering method for multivariate panel data characterized 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 toward the current center of their previous cluster assignment. 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.

Keywords: cluster membership persistence; dynamic clustering; insurance industry; shrinkage; silhouette index (search for similar items in EconPapers)
JEL-codes: C33 C38 G22 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Dynamic nonparametric clustering of multivariate panel data (2023)
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