Robust tests for heteroskedasticity in the one-way error components model

This paper constructs tests for heteroskedasticity in one-way error components models, in line with Baltagi, Bresson and Pirotte (Journal of Econometrics, 134, 2006). Our tests have two additional robustness properties. First, standard tests for heteroskedasticity in the individual component are shown to be negatively affected by heteroskedasticity in the remainder component. We derive modified tests that are insensitive to heteroskedasticity in the component not being checked, and hence help identify the source of heteroskedasticity. Second, Gaussian based LM tests are shown to reject too often in the presence of heavy-tailed (e.g. t-Student) distributions. By using a conditional moments framework, we derive distribution-free tests that are robust to non-normalities. Our tests are computationally convenient since they are based on simple artificial regressions after pooled OLS estimation.