论文标题
光谱群集和方差信息用于小组结构估计的面板数据
Spectral Clustering with Variance Information for Group Structure Estimation in Panel Data
论文作者
论文摘要
考虑一个面板数据设置，可以重复观察个体。通常，可以合理地假设存在一群具有相似效果的个人组，但分组通常是未知的。我们首先进行了局部分析，该分析揭示了单个系数估计的差异包含有用的群体结构估计信息。然后，我们提出了一种方法，以估算明确说明方差信息的通用面板数据模型的未观察到的分组。我们提出的方法在计算上仍然是可行的，并且对每个个体的大量个人和/或重复测量。即使没有单个级别的数据，也可以应用开发的思想，并且仅将参数估计以及对研究人员的估计不确定性进行了一定的量化。一项彻底的模拟研究表明，我们方法的性能优于现有方法，我们将方法应用于两个经验应用。
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.
