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Bandwidth selection for nonparametric regression with errors-in-variables

Dong, Hao, Otsu, Taisuke and Taylor, Luke (2022) Bandwidth selection for nonparametric regression with errors-in-variables. Econometric Reviews. ISSN 0747-4938 (In Press)

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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 of Delaigle and Hall (2008) in a Monte Carlo study. As well as dramatically reducing computational cost, the methods proposed in this paper lead to lower mean integrated squared error compared to the current state-of-the-art.

Item Type: Article
Official URL:
Additional Information: © 2022 Taylor and Francis.
Divisions: Economics
Date Deposited: 14 Jul 2022 09:03
Last Modified: 15 Nov 2022 00:16

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