Dynamic Nonparametric Clustering of Multivariate Panel Data

Research output: Contribution to JournalArticleAcademicpeer-review

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.
Original languageEnglish
Pages (from-to)335-374
Number of pages40
JournalJournal of Financial Econometrics
Volume22
Issue number2
DOIs
Publication statusPublished - 2024

Funding

I.C.J. and A.L. acknowledge support from the Dutch National Science Foundation (NWO) under grant 406.18.EB.011. J.S. acknowledges support from the Dutch National Science Foundation (NWO) under grant VI.VIDI.191.169. The views expressed in this paper are those of the authors and they do not necessarily reflect the views or policies of the European Central Bank.

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek406.18, VI.VIDI.191.169

    Keywords

    • cluster membership persistence
    • dynamic clustering
    • insurance industry
    • shrinkage
    • silhouette index

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