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Iterative Estimation of Nonparametric Regressions with Continuous Endogenous Variables and Discrete Instruments

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  • Samuele Centorrino
  • Fr'ed'erique F`eve
  • Jean-Pierre Florens

Abstract

We consider a nonparametric regression model with continuous endogenous independent variables when only discrete instruments are available that are independent of the error term. Although this framework is very relevant for applied research, its implementation is challenging, as the regression function becomes the solution to a nonlinear integral equation. We propose a simple iterative procedure to estimate such models and showcase some of its asymptotic properties. In a simulation experiment, we detail its implementation in the case when the instrumental variable is binary. We conclude with an empirical application to returns to education.

Suggested Citation

  • Samuele Centorrino & Fr'ed'erique F`eve & Jean-Pierre Florens, 2019. "Iterative Estimation of Nonparametric Regressions with Continuous Endogenous Variables and Discrete Instruments," Papers 1905.07812, arXiv.org, revised Oct 2024.
    • Handle: RePEc:arx:papers:1905.07812
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    Cited by:

    1. Emir Malikov & Shunan Zhao & Subal C. Kumbhakar, 2020. "Estimation of firm‐level productivity in the presence of exports: Evidence from China's manufacturing," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 457-480, June.
    2. Samuele Centorrino & Jean-Pierre Florens & Jean-Michel Loubes, 2022. "Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables," Papers 2202.08977, arXiv.org.

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