Random effects panel data models with known heteroskedasticity

The paper considers two estimators for the linear random effects panel data model with known heteroskedasticity. Examples where heteroskedasticity can be treated as given include panel regression with averaged data, meta regression and the linear probability model. While one estimator builds on the additive random effects assumption, the other, which is simpler to implement in standard software, assumes that the random effect is multiplied by the heteroskedastic standard deviation. Simulation results show that substantial efficiency gains can be realized with either of the two estimators, even in case of misspecification of the scedastic function. Correct confidence interval coverage is obtained if clustered standard errors are used. Efficiency gains are also evident in an illustrative meta-regression application estimating the effect of study design features on loss aversion coefficients.