Uniform confidence bands for nonparametric errors-in-variables regression

Kengo Kato and Yuya Sasaki

Journal of Econometrics, 2019, vol. 213, issue 2, 516-555

Abstract: This paper develops a method to construct uniform confidence bands for a nonparametric regression function where a predictor variable is subject to a measurement error. We allow for the distribution of the measurement error to be unknown, but assume the availability of validation data or repeated measurements on the latent predictor variable. The proposed confidence band builds on the deconvolution kernel estimation and a novel application of the multiplier bootstrap method. We establish asymptotic validity of the proposed confidence band. To our knowledge, this is the first paper to derive asymptotically valid uniform confidence bands for nonparametric errors-in-variables regression.

Keywords: Confidence band; Deconvolution; Errors-in-variables regression; Multiplier bootstrap (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:213:y:2019:i:2:p:516-555

DOI: 10.1016/j.jeconom.2019.05.021

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