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Dynamic Nonparametric Clustering of Multivariate Panel Data

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  • Igor Custodio João
  • Julia Schaumburg
  • André Lucas
  • Bernd Schwaab

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.

Suggested Citation

  • Igor Custodio João & Julia Schaumburg & André Lucas & Bernd Schwaab, 2024. "Dynamic Nonparametric Clustering of Multivariate Panel Data," Journal of Financial Econometrics, Oxford University Press, vol. 22(2), pages 335-374.
    • Handle: RePEc:oup:jfinec:v:22:y:2024:i:2:p:335-374.
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    References listed on IDEAS

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    1. Stéphane Bonhomme & Elena Manresa, 2015. "Grouped Patterns of Heterogeneity in Panel Data," Econometrica, Econometric Society, vol. 83(3), pages 1147-1184, May.
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    3. André Lucas & Julia Schaumburg & Bernd Schwaab, 2019. "Bank Business Models at Zero Interest Rates," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 542-555, July.
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    6. Leopoldo Catania, 2021. "Dynamic Adaptive Mixture Models with an Application to Volatility and Risk," Journal of Financial Econometrics, Oxford University Press, vol. 19(4), pages 531-564.
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    Cited by:

    1. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.
    2. Igor Custodio João, 2024. "Testing for Clustering Under Switching," Tinbergen Institute Discussion Papers 24-052/III, Tinbergen Institute.

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    More about this item

    Keywords

    cluster membership persistence; dynamic clustering; insurance industry; shrinkage; silhouette index;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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