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 |
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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: | 09 Dec 2024 17:09 |
URI: | http://eprints.lse.ac.uk/id/eprint/115551 |
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