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Nonparametric Instrumental Regressions with (Potentially Discrete) Instruments Independent of the Error Term

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

Abstract

We consider a nonparametric instrumental regression model with continuous endogenous regressor where instruments are fully independent of the error term. This assumption allows us to extend the reach of this model to cases where the instrumental variable is discrete, and therefore to substantially enlarge its potential empirical applications. Under our assumptions, the regression function becomes solution to a nonlinear integral equation. We contribute to existing literature by providing an exhaustive analysis of identification and a simple iterative estimation procedure. Details on the implementation and on the asymptotic properties of this estimation algorithm are given. We conclude the paper with a simulation experiment for a binary instrument and an empirical application to the estimation of the Engel curve for food, where we show that our estimator delivers results that are consistent with existing evidence under several discretizations of the instrumental variable.

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  • Samuele Centorrino & Fr'ed'erique F`eve & Jean-Pierre Florens, 2019. "Nonparametric Instrumental Regressions with (Potentially Discrete) Instruments Independent of the Error Term," Papers 1905.07812, arXiv.org.
  • Handle: RePEc:arx:papers:1905.07812
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    Cited by:

    1. Emir Malikov & Shunan Zhao & Subal C. Kumbhakar, 2020. "Estimation of firmā€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.

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