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A simple estimator for partial linear regression with endogenous nonparametric variables

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  • Delgado, Michael S.
  • Parmeter, Christopher F.

Abstract

We propose a simple kernel estimator for semiparametric partial linear models with endogeneity in the nonparametric function. Compared to the existing backfitting estimator, our estimator is notationally simpler and relatively easier to implement. We also discuss data-driven bandwidth selection to implement this estimator in practice. Monte Carlo exercises show that the finite sample performance of these two estimators is similar.

Suggested Citation

  • Delgado, Michael S. & Parmeter, Christopher F., 2014. "A simple estimator for partial linear regression with endogenous nonparametric variables," Economics Letters, Elsevier, vol. 124(1), pages 100-103.
    • Handle: RePEc:eee:ecolet:v:124:y:2014:i:1:p:100-103
    DOI: 10.1016/j.econlet.2014.04.032
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    References listed on IDEAS

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    Cited by:

    1. Das, Monica & Basu, Sudip Ranjan, 2022. "Understanding the relationship between income inequality and pollution: A fresh perspective with cross-country evidence," World Development Perspectives, Elsevier, vol. 26(C).

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