A general condition for an optimal limiting efficiency of OLS in the general linear regression model

We give a general condition for disturbance covariance matrices in the general linear regression model which ensures that, in the limit, ordinary least squares is as efficient as generalized least squares as the disturbance covariance matrix approaches the edges of the parameter space. This condition includes many known efficiency results as special cases.