Grouped Heterogeneity in Linear Panel Data Models with Heterogeneous Error Variances
提出一种利用误差方差分组来识别线性面板数据模型中潜在组结构的方法，并应用于企业研发投资与商业周期关系的研究，发现中型企业投资比大型企业更具顺周期性。
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 firm-level research and development (R&D) investments and the business cycle. We find a well-defined group structure in the variances that ex-post can be related to firm size. Our estimates indicate stronger procyclical investment patterns at medium-size firms compared to large firms.