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Polynomial regression with errors in the variables

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  • Chi‐Lung Cheng
  • Hans Schneeweiss

Abstract

A polynomial functional relationship with errors in both variables can be consistently estimated by constructing an ordinary least squares estimator for the regression coefficients, assuming hypothetically the latent true regressor variable to be known, and then adjusting for the errors. If normality of the error variables can be assumed, the estimator can be simplified considerably. Only the variance of the errors in the regressor variable and its covariance with the errors of the response variable need to be known. If the variance of the errors in the dependent variable is also known, another estimator can be constructed.

Suggested Citation

  • Chi‐Lung Cheng & Hans Schneeweiss, 1998. "Polynomial regression with errors in the variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 189-199.
    • Handle: RePEc:bla:jorssb:v:60:y:1998:i:1:p:189-199
    DOI: 10.1111/1467-9868.00118
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    Cited by:

    1. Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.
    2. Kukush, Alexander & Maschke, Erich Otto, 2003. "The efficiency of adjusted least squares in the linear functional relationship," Journal of Multivariate Analysis, Elsevier, vol. 87(2), pages 261-274, November.
    3. Wang, Liqun & Hsiao, Cheng, 2011. "Method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models," Journal of Econometrics, Elsevier, vol. 165(1), pages 30-44.
    4. Garcia, Tanya P. & Ma, Yanyuan, 2017. "Simultaneous treatment of unspecified heteroskedastic model error distribution and mismeasured covariates for restricted moment models," Journal of Econometrics, Elsevier, vol. 200(2), pages 194-206.
    5. Sergiy Shklyar & Hans Schneeweiss & Alexander Kukush, 2007. "Quasi Score is more Efficient than Corrected Score in a Polynomial Measurement Error Model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 65(3), pages 275-295, May.
    6. Stoker, Thomas M. & Berndt, Ernst R. & Denny Ellerman, A. & Schennach, Susanne M., 2005. "Panel data analysis of U.S. coal productivity," Journal of Econometrics, Elsevier, vol. 127(2), pages 131-164, August.
    7. Otsu, Taisuke & Taylor, Luke, 2021. "Specification Testing For Errors-In-Variables Models," Econometric Theory, Cambridge University Press, vol. 37(4), pages 747-768, August.
    8. Thomas Augustin & Helmut Küchenhoff & Matthias Schmid, 2022. "Nachruf Hans Schneeweiß," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(2), pages 149-154, June.
    9. Biørn, Erik, 2017. "Identification and Method of Moments Estimation in Polynomial Measurement Error Models," Memorandum 01/2017, Oslo University, Department of Economics.
    10. Hans Schneeweiss & Thomas Augustin, 2006. "Some recent advances in measurement error models and methods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 183-197, March.
    11. Schneeweiss, Hans & Cheng, Chi-Lun, 2006. "Bias of the structural quasi-score estimator of a measurement error model under misspecification of the regressor distribution," Journal of Multivariate Analysis, Elsevier, vol. 97(2), pages 455-473, February.
    12. Arturo Zavala & Heleno Bolfarine & Mário Castro, 2007. "Consistent estimation and testing in heteroscedastic polynomial errors-in-variables models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(3), pages 515-530, September.

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