A RMT-based LM test for error cross-sectional independence in large heterogeneous panel data models

Natalia Bailey, Dandan Jiang, Jianfeng Yao

Research output: Contribution to journalArticleResearchpeer-review


This paper introduces a new test for error cross-sectional independence in large panel data models with exogenous regressors having heterogenous slope coefficients. The proposed statistic, LMRMT, is based on the Lagrange Multiplier (LM) principle and the sample correlation matrix (Formula presented.) of the model’s residuals. Since in large panels (Formula presented.) poorly estimates its population counterpart, results from Random Matrix Theory (RMT) are used to establish the high-dimensional limiting distribution of LMRMT under heteroskedastic normal errors and assuming that both the panel size N and the sample size (Formula presented.) grow to infinity in comparable magnitude. Simulation results show that (Formula presented.) is largely correctly sized (except for some small values of N and T). Further, the empirical size and power outcomes show robustness of our statistic to deviations from the assumptions of normality for the error terms and of strict exogeneity for the regressors. The test has comparable small sample properties to related tests in the literature which have been developed under different asymptotic theory.

Original languageEnglish
Pages (from-to)564-582
Number of pages19
JournalEconometric Reviews
Issue number5
Publication statusPublished - 2022


  • Cross-sectional independence
  • High-dimensional Lagrange-Multiplier test
  • Large panels
  • Random Matrix theory

Cite this