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Abstract for:

Nonparametric Estimation of Additive Model with Errors-in-Variables

Hao  Dong,  Taisuke  Otsu,  November 2018
Paper No' EM600: | Full paper (pdf)
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Keywords: Additive model, Measurement error, Deconvolution

JEL Classification: C14; C13

Is hard copy/paper copy available? NO - Paper Copy Out Of Print.
This Paper is published under the following series: Econometrics
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Abstract:

In estimation of nonparametric additive models, conventional methods, such as backfitting and series approximation, cannot be applied when measurement errors are present in covariates. We propose an estimator for such models by extending Horowitz and Mammen’s (2004) two stage estimator for the errors-in-variables case. In the first stage, to adept to the additive structure, we use a series method together with a ridge approach to deal with ill-posedness brought by the mismeasurement. The uniform convergence rate for the first stage estimator is derived. To establish the limiting distribution, we consider the second stage estimator obtained by the one-step backfitting with a deconvolution kernel based on the first stage estimator.