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

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Author Info
Susanne M. Schennach (University of Chicago)
Yingyao Hu (Johns Hopkins University)
Arthur Lewbel () (Boston College)

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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.

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File URL: http://fmwww.bc.edu/EC-P/WP674.pdf
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Publisher Info
Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 674.

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Length: 21 pages
Date of creation: 16 Jul 2007
Date of revision:
Handle: RePEc:boc:bocoec:674

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Related research
Keywords: errors in variables; nonparametric estimation; identification;

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Find related papers by JEL classification:
C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  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. [Downloadable!] (restricted)
  2. Steven Klepper & Edward E. Leamer, 1982. "Consistent Sets of Estimates," UCLA Economics Working Papers 282, UCLA Department of Economics. [Downloadable!]
  3. 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. [Downloadable!] (restricted)
  4. 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.
  5. 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. [Downloadable!] (restricted)
  6. Andrew Chesher, 2000. "Polynomial Regression with Normal Covariate Measurement Error," Econometric Society World Congress 2000 Contributed Papers 1911, Econometric Society. [Downloadable!]
  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. [Downloadable!]
  8. Susanne M. Schennach, 2004. "Estimation of Nonlinear Models with Measurement Error," Econometrica, Econometric Society, vol. 72(1), pages 33-75, 01. [Downloadable!] (restricted)
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