Bandwidth selection for nonparametric regression with errors-in-variables

Hao Dong, Taisuke Otsu and Luke Taylor

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We propose two novel bandwidth selection procedures for the nonparametric regression model with classical measurement error in the regressors. Each method evaluates the prediction errors of the regression using a second (density) deconvolution. The first approach uses a typical leave-one-out cross-validation criterion, while the second applies a bootstrap approach and the concept of out-of-bag prediction. We show the asymptotic validity of both procedures and compare them to the SIMEX method in a Monte Carlo study. As well as dramatically reducing computational cost, the methods proposed in this article lead to lower mean integrated squared error (MISE) compared to the current state-of-the-art.

Keywords: measurement error models; deconvolution; nonparametric regression; bandwidth selection (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2023-04-22
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Econometric Reviews, 22, April, 2023, 42(4), pp. 393-419. ISSN: 0747-4938

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http://eprints.lse.ac.uk/115551/ Open access version. (application/pdf)

Related works:
Journal Article: Bandwidth selection for nonparametric regression with errors-in-variables (2023)
Working Paper: Bandwidth selection for nonparametric regression with errors-in-variables (2022)
Working Paper: Bandwidth Selection for Nonparametric Regression with Errors-in-Variables (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:115551

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