Variance estimation for the instrumental variables approach to measurement error in generalized linear models
James W. Hardin and
Raymond J. Carroll Additional contact information James W. Hardin: Arnold School of Public Health, University of South Carolina
Raymond J. Carroll: Department of Statistics, Texas A&M University
Abstract:
This paper derives and gives explicit formulas for a derived sandwich variance estimate. This variance estimate is appropriate for generalized linear additive measurement error models fitted using instrumental variables. We also generalize the known results for linear regression. As such, this article explains the theoretical justification for the sandwich estimate of variance utilized in the software for measurement error developed under the Small Business Innovation Research Grant (SBIR) by StataCorp. The results admit estimation of variance matrices for measurement error models where there is an instrument for the unknown covariate. Copyright 2003 by StataCorp LP.