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.

中文翻译：

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多元面板数据的动态非参数聚类

我们为多元面板数据引入了一种新的动态聚类方法，其特点是聚类位置和形状、聚类组成以及可能的聚类数量随时间变化。为了避免过于频繁的聚类切换（闪烁），我们扩展了标准的横截面聚类技术，并将观察值缩小到其先前聚类分配的当前中心。这将面板中的连续横截面连接在一起，大大减少了闪烁，并增强了结果的经济可解释性。我们以数据驱动的方式选择收缩参数，并在理论上以及在几个具有挑战性的模拟设置中研究其误分类特性。