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Measurement error in a single regressor

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  • Meijer, E.
  • Wansbeek, T.

    (Groningen University)

Abstract

For the setting of multiple regression with measurement error in a single regressor, we present some very simple formulas to assess the result that one may expect when correcting for measurement error. It is shown where the corrected estimated regression coefficients and the error variance may lie, and how the t-value behaves.

Suggested Citation

  • Meijer, E. & Wansbeek, T., 2000. "Measurement error in a single regressor," Research Report 00F14, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    • Handle: RePEc:gro:rugsom:00f14
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    File URL: http://irs.ub.rug.nl/ppn/240533909
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    References listed on IDEAS

    as
    1. Krasker, William S. & Pratt, John W., 1987. "Bounding the effects of proxy variables on instrumental-variables coefficients," Journal of Econometrics, Elsevier, vol. 35(2-3), pages 233-252, July.
    2. Krasker, William S & Pratt, John W, 1986. "Bounding the Effects of Proxy Variables on Regression Coefficients," Econometrica, Econometric Society, vol. 54(3), pages 641-655, May.
    3. Arthur Lewbel, 1997. "Constructing Instruments for Regressions with Measurement Error when no Additional Data are Available, with an Application to Patents and R&D," Econometrica, Econometric Society, vol. 65(5), pages 1201-1214, September.
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    Cited by:

    1. Mario Jametti & Thomas von Ungern-Sternberg, 2005. "Assessing the Efficiency of an Insurance Provider—A Measurement Error Approach," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 30(1), pages 15-34, June.
    2. Qing Li, 2014. "Identifiability of mean-reverting measurement error with instrumental variable," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(2), pages 118-129, May.
    3. Ramses H. ABUL NAGA, 2001. "Biases of the Ordinary Least Squares and Instrumental Variables Estimators of the Intergenerational Earnings Correlation : Revisited in the Light of Panel Data," Cahiers de Recherches Economiques du Département d'économie 01.05, Université de Lausanne, Faculté des HEC, Département d’économie.
    4. Erik Meijer & Edward Oczkowski & Tom Wansbeek, 2021. "How measurement error affects inference in linear regression," Empirical Economics, Springer, vol. 60(1), pages 131-155, January.
    5. Ramses Abul Naga, 2008. "Biases of the ordinary least squares and instrumental variables estimators of the intergenerational earnings elasticity: Revisited in the light of panel data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(4), pages 323-350, December.
    6. Titus J. Galama & Patrick Hullegie & Erik Meijer & Sarah Outcault, 2012. "Is There Empirical Evidence For Decreasing Returns To Scale In A Health Capital Model?," Health Economics, John Wiley & Sons, Ltd., vol. 21(9), pages 1080-1100, September.
    7. Galama, T. & Hullegie, P. & Meijer, E. & Outcault, S., 2012. "Empirical evidence for decreasing returns to scale in a health capital model," Health, Econometrics and Data Group (HEDG) Working Papers 12/05, HEDG, c/o Department of Economics, University of York.
    8. Jakob De Haan & Erik Leertouwer & Erik Meijer & Tom Wansbeek, 2003. "Measuring central bank independence: a latent variables approach," Scottish Journal of Political Economy, Scottish Economic Society, vol. 50(3), pages 326-340, August.
    9. Wansbeek, Tom, 2001. "GMM estimation in panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 104(2), pages 259-268, September.

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