Exact Inference for the Linear Model with Groupwise Heteroscedasticity

Exact inference on a single coefficient in a linear regression model, as introduced by Bekker (1997), is elaborated for the case of normally distributed heteroscedastic disturbances. Instead of approximate inference based on feasible generalized least squares, exact confidence sets are formulated based on partial rotational invariance of the distribution of the vector of disturbances. The approach is applied to the random-effects and fixed-effects models for panel data.