Consider a panel data setting where repeated observations on individuals are available. Often it is reasonable to assume that there exist groups of individuals that share similar effects of observed characteristics, but the grouping is typically unknown in advance. We first conduct a local analysis which reveals that the variances of the individual coefficient estimates contain useful information for the estimation of group structure. We then propose a method to estimate unobserved groupings for general panel data models that explicitly account for the variance information. Our proposed method remains computationally feasible with a large number of individuals and/or repeated measurements on each individual. The developed ideas can also be applied even when individual-level data are not available and only parameter estimates together with some quantification of estimation uncertainty are given to the researcher. A thorough simulation study demonstrates superior performance of our method than existing methods and we apply the method to two empirical applications.

翻译：考虑一个面板数据设置，其中可获得对个体的重复观测。通常可以合理假设存在若干个体群体，这些群体成员对观测特征具有相似的影响效应，但群体划分通常是事先未知的。我们首先进行局部性分析，揭示出个体系数估计的方差中包含用于估计群体结构的有用信息。随后，我们提出一种方法，用于在一般面板数据模型中显式利用方差信息估计未观测到的群体结构。所提出的方法在处理大量个体和/或每个个体的重复测量时仍保持计算可行性。即使研究者无法获取个体层面数据，仅能获得参数估计值及相应的估计不确定性量化指标时，所发展的思想同样适用。全面的仿真研究表明，我们的方法在性能上优于现有方法，并将该方法应用于两个实证案例。