Bandwidth selection for nonparametric regression with errors-in-variables

Hao Dong, Taisuke Otsu and Luke Taylor

Econometric Reviews, 2023, vol. 42, issue 4, 393-419

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.

Date: 2023
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Related works:
Working Paper: 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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DOI: 10.1080/07474938.2023.2191105

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