Eigenvalue-Based Randomness Test for Residual Diagnostics in Panel Data Models
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.
Cite
@article{arxiv.2504.05297,
title = {Eigenvalue-Based Randomness Test for Residual Diagnostics in Panel Data Models},
author = {Marcell T. Kurbucz and Betsabé Pérez Garrido and Antal Jakovác},
journal= {arXiv preprint arXiv:2504.05297},
year = {2026}
}
Comments
10 pages, 3 figures