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One-step nonparametric instrumental regression using smoothing splines

Author

Listed:
  • Beyhum, Jad
  • Lapenta, Elia
  • Lavergne, Pascal

Abstract

We extend nonparametric regression smoothing splines to a context where there is endogeneity and instrumental variables are available. Unlike popular existing es-timators, the resulting estimator is one-step and relies on a unique regularization parameter. We derive uniform rates of the convergence for the estimator and its first derivative. We also address the issue of imposing monotonicity in estimation. Sim-ulations confirm the good performances of our estimator compared to some popular two-step procedures. Our method yields economically sensible results when used to estimate Engel curves.

Suggested Citation

  • Beyhum, Jad & Lapenta, Elia & Lavergne, Pascal, 2023. "One-step nonparametric instrumental regression using smoothing splines," TSE Working Papers 23-1467, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:128467
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    References listed on IDEAS

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