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

Yingyao Hu, Arthur Lewbel and Susanne Schennach
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Yingyao Hu: Institute for Fiscal Studies and Johns Hopkins University

No CWP14/07, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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.

Date: 2007-07-26
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Working Paper: Nonparametric identification of the classical errors-in-variables model without side information (2007)
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