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

Griliches and Hausman 5 Griliches, Z. and Hausman, J. A. 1986. Errors in variables in panel data. J. Econometrics, 32: 93–118. [Crossref], [Web of Science ®] [Google Scholar] and Wansbeek 11 Wansbeek, T. J. 2001. GMM estimation in panel data models with measurement error. J. Econometrics, 104: 259–268. [Crossref], [Web of Science ®] [Google Scholar] 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.