eprintid: 50406
rev_number: 6
eprint_status: archive
userid: 1482
importid: 105
dir: disk0/00/05/04/06
datestamp: 2025-02-07 13:36:44
lastmod: 2025-02-07 16:31:05
status_changed: 2025-02-07 13:36:44
type: article
succeeds: 48208
metadata_visibility: show
creators_name: Beyhum, Jad
creators_name: Lapenta, Elia
creators_name: Lavergne, Pascal
creators_idrefppn: 255581564
creators_idrefppn: 255142803
creators_idrefppn: 177855533
creators_halaffid: 1002422
creators_halaffid: 1002422
creators_halaffid: 1002422
title: One-step smoothing splines instrumental regression
ispublished: pub
subjects: subjects_ECO
abstract: We extend nonparametric regression smoothing splines to a context where there is endogeneity and instrumental variables are available. Unlike popular existing estimators, the resulting estimator is one-step and relies on a unique regularization parameter. We derive rates of the convergence for the estimator and its first derivative, which are uniform in the support of the endogenous variable. We also address the issue of imposing monotonicity in estimation and extend the approach to a partly linear model. Simulations confirm the good performances of our estimator compared to two-step procedures. Our method yields economically sensible results when used to estimate Engel curves.
date: 2024
date_type: published
publisher: Elsevier
id_number: 10.1093/ectj/utae024
official_url: http://tse-fr.eu/pub/130306
faculty: tse
divisions: tse
language: en
has_fulltext: FALSE
doi: 10.1093/ectj/utae024
view_date_year: 2024
full_text_status: none
publication: The Econometrics Journal
place_of_pub: Amsterdam
refereed: TRUE
issn: 0304-4076
oai_identifier: oai:tse-fr.eu:130306
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oai_lastmod: 2025-02-05T13:40:19Z
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site: ut1
hal_id: hal-04935697
hal_passwd: 5#qvc9
hal_version: 1
hal_url: https://hal.science/hal-04935697
citation:   Beyhum, Jad <https://www.idref.fr/255581564>, Lapenta, Elia <https://www.idref.fr/255142803> and Lavergne, Pascal <https://www.idref.fr/177855533>  (2024) One-step smoothing splines instrumental regression.    The Econometrics Journal.