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Variance estimation for the instrumental variables approach to measurement error in generalized linear models

Author

Listed:
  • 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.

Suggested Citation

  • James W. Hardin & Raymond J. Carroll, 2003. "Variance estimation for the instrumental variables approach to measurement error in generalized linear models," Stata Journal, StataCorp LP, vol. 3(4), pages 342-350, December.
    • Handle: RePEc:tsj:stataj:v:3:y:2003:i:4:p:342-350
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    References listed on IDEAS

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    1. Minge Xie & Douglas G. Simpson & Raymond J. Carroll, 2000. "Random Effects in Censored Ordinal Regression: Latent Structure and Bayesian Approach," Biometrics, The International Biometric Society, vol. 56(2), pages 376-383, June.
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    Cited by:

    1. Udi Sommer, 2011. "How rational are justices on the Supreme Court of the United States? Doctrinal considerations during agenda setting," Rationality and Society, , vol. 23(4), pages 452-477, November.
    2. Zhao, Zhenxiang & Kaestner, Robert, 2010. "Effects of urban sprawl on obesity," Journal of Health Economics, Elsevier, vol. 29(6), pages 779-787, December.

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