Dynamic nonparametric clustering of multivariate panel data

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 clusterin...

Full description

Saved in:
Bibliographic Details
Published in:Policy File 2023
Main Authors: Igor Custodio Joao, Lucas, André, Schaumburg, Julia, Schwaab, Bernd
Format: Report
Language:English
Subjects:
Clustering
Data analysis
Economic models
Finance
Insurance
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 towards 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.