Testing for correlation in error‐component models
针对分组数据，提出一种检验组内相关性的方法，允许异方差和内生变量，适用于非平衡面板，在模拟和实际数据中表现优于现有方法。
Summary This paper concerns linear models for grouped data with group‐specific effects. We construct a portmanteau test for the null of no within‐group correlation beyond that induced by the group‐specific effect. The approach allows for heteroskedasticity and is applicable to models with exogenous, predetermined, or endogenous regressors. The test can be implemented as soon as three observations per group are available and is applicable to unbalanced data. A test with such general applicability is not available elsewhere. We provide theoretical results on size and power under asymptotics where the number of groups grows but their size is held fixed. Extensive power comparisons with other tests available in the literature for special cases of our setup reveal that our test compares favorably. In a simulation study we find that, under heteroskedasticity, only our procedure yields a test that is both size correct and powerful. In a large data set on mothers with multiple births we find that infant birthweight is correlated across children even after controlling for mother fixed effects and a variety of prenatal care factors. This suggests that such a strategy may be inadequate to take care of all confounding factors that correlate with the mother's decision to engage in activities that are detrimental to the infant's health, such as smoking.