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Abstract:

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Econometrics Paper
Nonparametric Estimation of Additive Model with Errors-in-Variables
Hao Dong and Taisuke Otsu
November 2018
Paper No' EM600:
Full Paper (pdf)

JEL Classification: C14; C13


Tags: additive model; measurement error; deconvolution

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.