A New Approach for Homogeneity Pursuit in Short Panel Data Analysis
提出一种适用于长面板和短面板数据的同质性追踪新方法，在聚类规模很小时仍优于现有方法，并通过模拟和实际数据验证其有效性。
In panel data analysis, individual attributes are of importance in many real applications. With the advancement of data collection, it is often possible to acquire enough information for individual attributes in a collected panel dataset, and data from other individuals may contain the information for the attributes of the individual under concern. Homogeneity pursuit is an important topic in panel data analysis when individual attributes are of interest. Existing approaches are mainly based on either penalized estimation or binary segmentation, and require reasonably large cluster sizes. However, in practice, people often come across panel datasets with small cluster sizes, i.e. short panel datasets. In this paper, we propose a new approach to homogeneity pursuit in panel data analysis, which applies to both long and short panel datasets. Our approach differs from existing methods and enjoys good asymptotic properties that justify its adoption. Extensive simulation studies show that the new approach works very well even when cluster sizes are too small to get any estimators based on one individual, outperforming all alternative methods in all conducted cases. Finally, we apply the new approach to a real dataset and illustrate its practical usefulness and superiority.