A REVIEW AND COMPARISON OF TESTS OF CROSS‐SECTION INDEPENDENCE IN PANELS
综述并比较了面板回归模型扰动项截面独立性的诊断检验，通过蒙特卡洛实验和健康数据实证，发现基于平均配对相关系数的检验在因子模型备择假设下表现良好，而基于间距的检验能识别强截面依赖但对空间相关性功效较低。
Abstract In this paper we review and compare diagnostic tests of cross‐section independence in the disturbances of panel regression models. We examine tests based on the sample pairwise correlation coefficient or on its transformations, and tests based on the theory of spacings. The ultimate goal is to shed some light on the appropriate use of existing diagnostic tests for cross‐equation error correlation. Our discussion is supported by means of a set of Monte Carlo experiments and a small empirical study on health. Results show that tests based on the average of pairwise correlation coefficients work well when the alternative hypothesis is a factor model with non‐zero mean loadings. Tests based on spacings are powerful in identifying various forms of strong cross‐section dependence, but have low power when they are used to capture spatial correlation.