GMM in linear regression for longitudinal data with multiple covariates measured with error

Zhiguo Xiao (), Jun Shao and Mari Palta

Journal of Applied Statistics, 2010, vol. 37, issue 5, 791-805

Abstract: Griliches and Hausman 5 and Wansbeek 11 proposed using the generalized method of moments (GMM) to obtain consistent estimators in linear regression models for longitudinal data with measurement error in one covariate, without requiring additional validation or replicate data. For usefulness of this methodology, we must extend it to the more realistic situation where more than one covariate are measured with error. Such an extension is not straightforward, since measurement errors across different covariates may be correlated. By a careful construction of the measurement error correlation structure, we are able to extend Wansbeek's GMM and show that the extended Griliches and Hausman's GMM is equivalent to the extended Wansbeek's GMM. For illustration, we apply the extended GMM to data from two medical studies, and compare it with the naive method and the method assuming only one covariate having measurement error.

Keywords: longitudinal data; multiple covariates; measurement error; generalized method of moments (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (4)

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DOI: 10.1080/02664760902890005

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