Grouped Heterogeneity in Linear Panel Data Models with Heterogeneous Error Variances
异质误差方差下线性面板模型的分组异质性
We develop a procedure to identify latent group structures in linear panel data models that exploits a grouping in the error variances of cross-sectional units. To accommodate such grouping, we introduce an objective function that avoids a singularity that arises in a pseudolikelihood approach. We provide theoretical and numerical evidence showing when allowing for variance groups improves classification. The developed procedure provides new evidence on the relation between…