 A RMT-based LM test for error cross-sectional independence in large heterogeneous panel data models*Natalia Bailey, Dandan Jiang and Jianfeng Yao Econometric Reviews, 2022, vol. 41, issue 5, 564-582 Abstract: 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 R^N of the model’s residuals. Since in large panels R^N 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 T grow to infinity in comparable magnitude. Simulation results show that LMRMT 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. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:41:y:2022:i:5:p:564-582 Ordering information: This journal article can be ordered from DOI: 10.1080/07474938.2021.2009705 Access Statistics for this article Econometric Reviews is currently edited by Dr. Essie Maasoumi More articles in Econometric Reviews from Taylor & Francis Journals |
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