Robust random effects tests for two-way error component models with panel data

In this paper, two test statistics are constructed respectively for individual and time effects in linear panel data models by comparing estimators of the variance of the idiosyncratic error at different robust levels. The resultant tests are one-sided, and asymptotically normally distributed under the null hypothesis. Power study shows that the tests can detect local alternatives that differ from the null hypothesis at the parametric rate. Due to the first difference and orthogonal transformations used in the construction of variance estimators of the idiosyncratic error, the two proposed tests are robust to the presence of one effect and the possible correlation between the covariates and the error components when the other one is tested. Monte Carlo simulations are carried out to provide evidence on the finite sample properties of the tests.