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

  • Susanne Schennach

    ()

    (Institute for Fiscal Studies and University of Chicago)

  • Yingyao Hu
  • Arthur Lewbel

    (Institute for Fiscal Studies and Boston College)

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.

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File URL: http://cemmap.ifs.org.uk/wps/cwp1407.pdf
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP14/07.

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Date of creation: Jul 2007
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Handle: RePEc:ifs:cemmap:14/07
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  1. 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.
  2. Dagenais, Marcel G. & Dagenais, Denyse L., 1997. "Higher moment estimators for linear regression models with errors in the variables," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 193-221.
  3. 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.
  4. 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.
  5. Susanne M. Schennach, 2004. "Estimation of Nonlinear Models with Measurement Error," Econometrica, Econometric Society, vol. 72(1), pages 33-75, 01.
  6. Yingyao Hu & Susanne 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.
  7. 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.
  8. Steven Klepper & Edward E. Leamer, 1982. "Consistent Sets of Estimates," UCLA Economics Working Papers 282, UCLA Department of Economics.
  9. 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.
  10. Andrew Chesher, 2000. "Polynomial Regression with Normal Covariate Measurement Error," Econometric Society World Congress 2000 Contributed Papers 1911, Econometric Society.
  11. 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.
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