Dynamic clustering of multivariate panel data

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

Journal of Econometrics, 2023, vol. 237, issue 2

Abstract: We propose a dynamic clustering model for uncovering latent time-varying group structures in multivariate panel data. The model is dynamic in three ways. First, the cluster location and scale matrices are time-varying to track gradual changes in cluster characteristics over time. Second, all units can transition between clusters based on a Hidden Markov model (HMM). Finally, the HMM’s transition matrix can depend on lagged time-varying cluster distances as well as economic covariates. Monte Carlo experiments suggest that the units can be classified reliably in a variety of challenging settings. Incorporating dynamics in the cluster composition proves empirically important in a study of 299 European banks between 2008Q1 and 2018Q2. We find that approximately 3% of banks transition per quarter on average. Transition probabilities are in part explained by differences in bank profitability, suggesting that factors contributing to low profitability for some banks can lead to long-lasting changes in financial industry structure.

Keywords: Dynamic clustering; Panel data; Hidden Markov Model; Score-driven dynamics; Bank business models (search for similar items in EconPapers)
Date: 2023
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Related works:
Working Paper: Dynamic clustering of multivariate panel data (2021)
Working Paper: Dynamic clustering of multivariate panel data (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:237:y:2023:i:2:s0304407622000689

DOI: 10.1016/j.jeconom.2022.03.003

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