Homogeneity pursuit in panel data models: Theory and application
研究如何将面板数据中的个体按斜率参数的同质性分组，提出Panel-CARDS算法，能同时识别真实分组结构并一致估计参数，模拟和实证验证了方法效果。
Summary This paper studies the estimation of a panel data model with latent structures where individuals can be classified into different groups with the slope parameters being homogeneous within the same group but heterogeneous across groups. To identify the unknown group structure of vector parameters, we design an algorithm called Panel‐CARDS. We show that it can identify the true group structure asymptotically and estimate the model parameters consistently at the same time. Simulations evaluate the performance and corroborate the asymptotic theory in several practical design settings. The empirical application reveals the heterogeneous grouping effect of income on democracy.