Cookies?
Library Header Image
LSE Research Online LSE Library Services

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)

[img] Text (Bag11) - Accepted Version
Pending embargo until 1 January 2100.

Download (621kB) | Request a copy

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 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: https://www.tandfonline.com/journals/lecr20
Additional Information: © 2022 Taylor and Francis.
Divisions: Economics
Date Deposited: 14 Jul 2022 09:03
Last Modified: 15 Nov 2022 00:16
URI: http://eprints.lse.ac.uk/id/eprint/115551

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics