Bandwidth selection for nonparametric regression with errors-in-variables

Dong, Hao, Otsu, Taisuke ORCID: 0000-0002-2307-143X and Taylor, Luke (2023) Bandwidth selection for nonparametric regression with errors-in-variables. Econometric Reviews, 42 (4). pp. 393-419. ISSN 0747-4938

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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.

Item Type:	Article
Official URL:	https://www.tandfonline.com/journals/lecr20
Additional Information:	© 2022 Taylor and Francis.
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory
JEL classification:	C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods
Date Deposited:	14 Jul 2022 09:03
Last Modified:	25 Nov 2024 08:25
URI:	http://eprints.lse.ac.uk/id/eprint/115551

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