Generalized Automatic Least Squares: Efficiency Gains from Misspecified Heteroscedasticity Models

Résumé 0

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