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Nonparametric identification of the classical errors-in-variables model without side information

Author

Listed:
  • Susanne M. Schennach

    (University of Chicago)

  • Yingyao Hu

    (Johns Hopkins University)

  • Arthur Lewbel

    (Boston College)

Abstract

This note establishes that the fully nonparametric classical errors-in-variables model is identifiable from data on the regressor and the dependent variable alone, unless the specification is a member of a very specific parametric family. This family includes the linear specification with normally distributed variables as a special case. This result relies on standard primitive regularity conditions taking the form of smoothness and monotonicity of the regression function and nonvanishing characteristic functions of the disturbances.

Suggested Citation

  • Susanne M. Schennach & Yingyao Hu & Arthur Lewbel, 2007. "Nonparametric identification of the classical errors-in-variables model without side information," Boston College Working Papers in Economics 674, Boston College Department of Economics.
    • Handle: RePEc:boc:bocoec:674
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    References listed on IDEAS

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    1. Timothy Erickson & Toni M. Whited, 2000. "Measurement Error and the Relationship between Investment and q," Journal of Political Economy, University of Chicago Press, vol. 108(5), pages 1027-1057, October.
    2. Susanne M. Schennach, 2004. "Estimation of Nonlinear Models with Measurement Error," Econometrica, Econometric Society, vol. 72(1), pages 33-75, January.
    3. Yingyao Hu & Susanne M. Schennach, 2006. "Identification and estimation of nonclassical nonlinear errors-in-variables models with continuous distributions using instruments," CeMMAP working papers CWP17/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Whitney K. Newey, 2001. "Flexible Simulated Moment Estimation Of Nonlinear Errors-In-Variables Models," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 616-627, November.
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    6. 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.
    7. Geert Ridder & Yingyao Hu, 2004. "Estimation of Nonlinear Models with Measurement Error Using Marginal Information," Econometric Society 2004 North American Summer Meetings 21, Econometric Society.
    8. Klepper, Steven & Leamer, Edward E, 1984. "Consistent Sets of Estimates for Regressions with Errors in All Variables," Econometrica, Econometric Society, vol. 52(1), pages 163-183, January.
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    10. Pal, Manoranjan, 1980. "Consistent moment estimators of regression coefficients in the presence of errors in variables," Journal of Econometrics, Elsevier, vol. 14(3), pages 349-364, December.
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    12. Hausman, Jerry A. & Newey, Whitney K. & Ichimura, Hidehiko & Powell, James L., 1991. "Identification and estimation of polynomial errors-in-variables models," Journal of Econometrics, Elsevier, vol. 50(3), pages 273-295, December.
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    Cited by:

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    More about this item

    Keywords

    errors in variables; nonparametric estimation; identification;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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