arXiv Open Access 2023

One-step smoothing splines instrumental regression

Jad Beyhum Elia Lapenta Pascal Lavergne

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Abstrak

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.

Topik & Kata Kunci

econ.EM stat.ME

Penulis (3)

Jad Beyhum

Elia Lapenta

Pascal Lavergne

Format Sitasi

APA MLA BibTeX

Beyhum, J., Lapenta, E., Lavergne, P. (2023). One-step smoothing splines instrumental regression. https://arxiv.org/abs/2307.14867

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓