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In linear models, the generalized least squares (GLS) estimator is applicablewhen the structure of the error dependence is known. When it is unknown, suchstructure must be approximated and estimated in a manner that may lead tomisspecification. The large-sample analysis of incorrectly-specified GLS (IGLS)estimators requires careful asymptotic manipulations. When performingestimation in the frequency domain, the asymptotic normality of the IGLSestimator, under the so-called Grenander assumptions, has been proved for abroad class of error dependence models. Under the same assumptions, asymptoticnormality results for the time-domain IGLS estimator are only available for alimited class of error structures. We prove that the time-domain IGLS estimatoris asymptotically normal for a general class of dependence models.

Asymptotic normality of the time-domain generalized least squares estimator for linear regression models