Abstract:
It is well known that in standard linear regression models with independent and identically distributed data and homoskedasticity, adding hurts (asymptotic) efficiency unless such irrelevant regressors are orthogonal to the remaining regressors. But we have found that under (conditional) heteroskedasticity can always be found such that one can achieve the asymptotic variance of the generalized least squares estimator by adding the to the model.
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