A moment-based test for individual effects in the error component model with incomplete panels

This paper proposes a robust test statistic for individual effects in the error component model with incomplete panel data. Specifically, on the base of the difference of variance estimators of the idiosyncratic errors at different levels, we construct a statistic to test for the existence of individual effects, which can be shown to asymptotically normally distributed under the null hypothesis. Power study shows that the test can detect local alternatives distinct at the parametric rate from the null hypothesis and has a larger asymptotic power than the corresponding ANOVA FF test when the individual heterogeneity effects are correlated with regressors. Monte Carlo evidence shows that the test statistic has desired finite sample properties. A real data example is analyzed for illustration.