Title: Can the Wild Bootstrap be Tamed into a General Analysis of Covariance Model?,
Abstract: It is well known that the F test is severly affected by heteroskedasticity in
unbalanced analysis of covariance (ANCOVA) models. Currently available remedies
for such a scenario are either based on heteroskedasticity-consistent
covariance matrix estimation (HCCME) or bootstrap techniques. However, the
HCCME approach tends to be liberal in small samples. Therefore, we propose a
combination of HCCME and a wild bootstrap technique. We prove the theoretical
validity of our approach and investigate its performance in an extensive
simulation study in comparison to existing procedures. The results indicate
that our proposed test remedies all problems of the ANCOVA F test and its
heteroskedasticityconsistent alternatives. Our test only requires very general
conditions, thus being applicable in a broad range of real-life settings.