Identification of Latent Subgroups for Time-Varying Panel Data Models
时变面板数据模型中的潜在子群识别
This article introduces a time-varying panel data model that incorporates latent group structures, designed to tackle both individual heterogeneity and smooth structural changes over time. We develop an innovative center-augmented K-power means (KPM) methodology that promotes convergence of subjects toward their respective cluster centers, enabling the identification of latent group structures without requiring prior knowledge of group composition. This approach delivers…