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It is well known that in the presence of heteroscedasticity ordinary leastsquares estimator is not efficient. I propose a generalized automatic leastsquares estimator (GALS) that makes partial correction of heteroscedasticitybased on a (potentially) misspecified model without a pretest. Such anestimator is guaranteed to be at least as efficient as either OLS or WLS butcan provide some asymptotic efficiency gains over OLS if the misspecified modelis approximately correct. If the heteroscedasticity model is correct, theproposed estimator achieves full asymptotic efficiency. The idea is to framemoment conditions corresponding to OLS and WLS squares based on miss-specifiedheteroscedasticity as a joint generalized method of moments estimation problem.The resulting optimal GMM estimator is equivalent to a feasible GLS withestimated weight matrix. I also propose an optimal GMM variance-covarianceestimator for GALS to account for any remaining heteroscedasticity in theresiduals.

Generalized Automatic Least Squares: Efficiency Gains from Misspecified Heteroscedasticity Models