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Rank-based tests of cross-sectional dependence in panel data models

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  • Feng, Long
  • Zhao, Ping
  • Ding, Yanling
  • Liu, Binghui

Abstract

In the study of panel regression, current existing cross-sectional dependence tests are mainly based on the normal assumption. However, in practical applications, the normal assumption is usually not valid, which weakens the usability of the tests. To develop more testing tools suitable for nonnormal panel data, we extend the rank-based framework of U-statistics to panel regressions, and derive their asymptotic null distributions respectively as (N,T)→∞. The results of some simulation results and a real data analysis demonstrate the superiority of the proposed tests, especially their robustness to deviation from normality.

Suggested Citation

  • Feng, Long & Zhao, Ping & Ding, Yanling & Liu, Binghui, 2021. "Rank-based tests of cross-sectional dependence in panel data models," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:csdana:v:153:y:2021:i:c:s0167947320301614
    DOI: 10.1016/j.csda.2020.107070
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    References listed on IDEAS

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    3. 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.

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