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Semi-parametric estimation in a single-index model with endogenous variables

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  • Mélanie BIRKE
  • Sébastien VAN BELLEGEM
  • Ingrid VAN KEILEGOM

Abstract

We consider a semiparametric single-index model, and suppose that endogeneity is present in the explanatory variables. The presence of an instrument is assumed that is non-correlated with the error term. We propose an estimator of the parametric component of the model, which is the solution of an ill-posed inverse problem. The estimator is shown to be asymptotically normal under certain regularity conditions. A simulation study is conducted to illustrate the finite sample performance of the proposed estimator.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Mélanie BIRKE & Sébastien VAN BELLEGEM & Ingrid VAN KEILEGOM, 2017. "Semi-parametric estimation in a single-index model with endogenous variables," LIDAM Reprints CORE 2898, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:2898
    Note: In : Scandinavian Journal of Statistics. Theory and Applications, 44, 168-191, 2017
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    Cited by:

    1. Xin Geng & Carlos Martins-Filho & Feng Yao, 2015. "Estimation of a Partially Linear Regression in Triangular Systems," Working Papers 15-46, Department of Economics, West Virginia University.
    2. Zhang, Hong-Fan, 2021. "Iterative GMM for partially linear single-index models with partly endogenous regressors," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    3. Alan P. Ker & Abdoul G. Sam, 2018. "Semiparametric estimation of the link function in binary-choice single-index models," Computational Statistics, Springer, vol. 33(3), pages 1429-1455, September.
    4. Senay Sokullu & Irene Botosaru & Chris Muris, 2022. "Time-Varying Linear Transformation Models with Fixed Effects and Endogeneity for Short Panels," Bristol Economics Discussion Papers 22/756, School of Economics, University of Bristol, UK.
    5. Muhammad Qasim, 2024. "A weighted average limited information maximum likelihood estimator," Statistical Papers, Springer, vol. 65(5), pages 2641-2666, July.

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