Grouped Heterogeneity in Linear Panel Data Models with Heterogeneous Error Variances

Jhordano Aguilar Loyo and Tom Boot

Journal of Business & Economic Statistics, 2025, vol. 43, issue 1, 68-80

Abstract: 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.

Date: 2025
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DOI: 10.1080/07350015.2024.2325440

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