Panel Sequential Group Estimation of Interactive Effects Models
提出一种新方法，用于识别面板数据模型中斜率项的潜在分组，该方法仅依赖闭式估计量，计算效率高，适用于大数据集，并通过模拟和实证验证了其一致性和有效性。
ABSTRACT This paper proposes a novel procedure to identify latent groups in the slopes of panel data models with interactive effects. The method is straightforward to apply and relies only on closed‐form estimators when evaluating the objective function. This achieves substantial computational gains compared to alternative non‐linear methods and enables estimation even in large datasets. We establish consistency of the procedure and confirm through simulations its good finite sample properties and the validity of post‐grouping inference routines. The procedure's empirical usefulness is illustrated with applications to output growth and international trade.