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Nonparametric Estimation of Additive Model with Errors-in-Variables

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  • Hao Dong
  • Taisuke Otsu

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.

Suggested Citation

  • Hao Dong & Taisuke Otsu, 2018. "Nonparametric Estimation of Additive Model with Errors-in-Variables," STICERD - Econometrics Paper Series 600, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    • Handle: RePEc:cep:stiecm:600
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    References listed on IDEAS

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    Cited by:

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    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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