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Improving survey-weighted least squares regression

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The weighted least squares (WLS) estimator is often employed in linear regression using complex survey data to deal with the bias in ordinary least squares (OLS) arising from informative sampling. In this paper a ‘quasi-Aitken WLS’ (QWLS) estimator is proposed. QWLS modifies WLS in the same way that Cragg’s quasi-Aitken estimator modifies OLS. It weights by the usual inverse sample inclusion probability weights multiplied by a parameterized function of covariates, where the parameters are chosen to minimize a variance criterion. The resulting estimator is consistent for the superpopulation regression coefficient under fairly mild conditions and has a smaller asymptotic variance than WLS.

Keywords: Complex survey data; Heteroscedasticity; Quasi-Aitken estimation; Weighted least squares

Document Type: Original Article


Affiliations: McMaster University, Hamilton, Canada

Publication date: January 1, 1998

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