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Grouped Heterogeneity in Linear Panel Data Models with Heterogeneous Error Variances

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  • Jhordano Aguilar Loyo
  • Tom Boot

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.

Suggested Citation

  • Jhordano Aguilar Loyo & Tom Boot, 2025. "Grouped Heterogeneity in Linear Panel Data Models with Heterogeneous Error Variances," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 43(1), pages 68-80, January.
    • Handle: RePEc:taf:jnlbes:v:43:y:2025:i:1:p:68-80
    DOI: 10.1080/07350015.2024.2325440
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