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Eigenvalue-Based Randomness Test for Residual Diagnostics in Panel Data Models

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  • Marcell T. Kurbucz
  • Betsab'e P'erez Garrido
  • Antal Jakov'ac

Abstract

This paper introduces the Eigenvalue-Based Randomness (EBR) test - a novel approach rooted in the Tracy-Widom law from random matrix theory - and applies it to the context of residual analysis in panel data models. Unlike traditional methods, which target specific issues like cross-sectional dependence or autocorrelation, the EBR test simultaneously examines multiple assumptions by analyzing the largest eigenvalue of a symmetrized residual matrix. Monte Carlo simulations demonstrate that the EBR test is particularly robust in detecting not only standard violations such as autocorrelation and linear cross-sectional dependence (CSD) but also more intricate non-linear and non-monotonic dependencies, making it a comprehensive and highly flexible tool for enhancing the reliability of panel data analyses.

Suggested Citation

  • Marcell T. Kurbucz & Betsab'e P'erez Garrido & Antal Jakov'ac, 2025. "Eigenvalue-Based Randomness Test for Residual Diagnostics in Panel Data Models," Papers 2504.05297, arXiv.org.
    • Handle: RePEc:arx:papers:2504.05297
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    References listed on IDEAS

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