Nonparametric instrumental regression with errors in variables

Karun Adusumilli and Taisuke Otsu

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: This paper considers nonparametric instrumental variable regression when the endogenous variable is contaminated with classical measurement error. Existing methods are inconsistent in the presence of measurement error. We propose a wavelet deconvolution estimator for the structural function that modifies the generalized Fourier coefficients of the orthogonal series estimator to take into account the measurement error. We establish the convergence rates of our estimator for the cases of mildly/severely ill-posed models and ordinary/super smooth measurement errors. We characterize how the presence of measurement error slows down the convergence rates of the estimator. We also study the case where the measurement error density is unknown and needs to be estimated, and show that the estimation error of the measurement error density is negligible under mild conditions as far as the measurement error density is symmetric.

Keywords: Nonparametric instrumental variable regression; measurement error; inverse problem; deconvolution; measurement error (search for similar items in EconPapers)
JEL-codes: C26 (search for similar items in EconPapers)
Date: 2015-07
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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https://sticerd.lse.ac.uk/dps/em/em585.pdf (application/pdf)

Related works:
Working Paper: Nonparametric instrumental regression with errors in variables (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:/2015/585

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