A RMT-based LM test for error cross-sectional independence in large heterogeneous panel data models*
提出一种基于随机矩阵理论的LM检验（LMRMT），用于检验大规模异质面板数据模型中误差项的截面独立性，适用于N和T同阶增长的情形，模拟显示检验在非正态和弱外生假设下仍稳健。
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.