Grouped Patterns of Heterogeneity in Panel Data
在线性面板数据模型中引入随时间变化的分组异质性模式，使用分组固定效应估计量，并允许组别归属不受限制，适用于研究收入与民主等关系。
This paper introduces time-varying grouped patterns of heterogeneity in linear panel data models. A distinctive feature of our approach is that group membership is left unrestricted. We estimate the parameters of the model using a "grouped fixed-effects" estimator that minimizes a least squares criterion with respect to all possible groupings of the cross-sectional units. Recent advances in the clustering literature allow for fast and efficient computation. We provide conditions under which our estimator is consistent as both dimensions of the panel tend to infinity, and we develop inference methods. Finally, we allow for grouped patterns of unobserved heterogeneity in the study of the link between income and democracy across countries.