Nonparametric estimation of additive models with errors-in-variables

Dong, Hao, Otsu, Taisuke and Taylor, Luke (2022) Nonparametric estimation of additive models with errors-in-variables. Econometric Reviews, 41 (10). 1164 - 1204. ISSN 0747-4938

Text (NpAdd_v11) - Accepted Version Available under License Creative Commons Attribution Non-commercial. Download (726kB)

• Author
• Author
• Scopus publication

Abstract

In the estimation of nonparametric additive models, conventional methods, such as backfitting and series approximation, cannot be applied when measurement error is present in a covariate. This paper proposes a two-stage estimator for such models. In the first stage, to adapt to the additive structure, we use a series approximation together with a ridge approach to deal with the ill-posedness brought by mismeasurement. We derive the uniform convergence rate of this first-stage estimator and characterize how the measurement error slows down the convergence rate for ordinary/super smooth cases. To establish the limiting distribution, we construct a second-stage estimator via one-step backfitting with a deconvolution kernel using the first-stage estimator. The asymptotic normality of the second-stage estimator is established for ordinary/super smooth measurement error cases. Finally, a Monte Carlo study and an empirical application highlight the applicability of the estimator.

Item Type:	Article
Official URL:	https://www.tandfonline.com/journals/lecr20
Additional Information:	© 2022 Taylor & Francis Group, LLC
Divisions:	Economics
Subjects:	H Social Sciences > HB Economic Theory
JEL classification:	C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C10 - General
Date Deposited:	17 Aug 2022 10:24
Last Modified:	08 Apr 2024 02:57
URI:	http://eprints.lse.ac.uk/id/eprint/116007

Actions (login required)

View Item