Nonparametric estimation of additive models with errors-in-variables

Hao Dong, Taisuke Otsu, Luke Taylor

研究成果: Article査読

抄録

In the estimation of nonparametric additive models, conventional methods, such as backfitting and series approximation, cannot be applied when measurement error is present in a covariate. This paper proposes a two-stage estimator for such models. In the first stage, to adapt to the additive structure, we use a series approximation together with a ridge approach to deal with the ill-posedness brought by mismeasurement. We derive the uniform convergence rate of this first-stage estimator and characterize how the measurement error slows down the convergence rate for ordinary/super smooth cases. To establish the limiting distribution, we construct a second-stage estimator via one-step backfitting with a deconvolution kernel using the first-stage estimator. The asymptotic normality of the second-stage estimator is established for ordinary/super smooth measurement error cases. Finally, a Monte Carlo study and an empirical application highlight the applicability of the estimator.

本文言語English
ページ(範囲)1164-1204
ページ数41
ジャーナルEconometric Reviews
41
10
DOI
出版ステータスPublished - 2022
外部発表はい

ASJC Scopus subject areas

  • 経済学、計量経済学

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