Dynamic Nonparametric Clustering of Multivariate Panel Data
提出一种新的动态聚类方法，处理多元面板数据中聚类位置、形状、组成和数量随时间变化的问题，通过惩罚项减少频繁切换，增强经济可解释性，并用欧洲保险公司数据验证。
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.