One-step smoothing splines instrumental regression
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866929620133085184 |
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| author | Beyhum, Jad Lapenta, Elia Lavergne, Pascal |
| author_facet | Beyhum, Jad Lapenta, Elia Lavergne, Pascal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_14867 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | One-step smoothing splines instrumental regression Beyhum, Jad Lapenta, Elia Lavergne, Pascal Econometrics Methodology 62G05 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. |
| title | One-step smoothing splines instrumental regression |
| topic | Econometrics Methodology 62G05 |
| url | https://arxiv.org/abs/2307.14867 |