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Estimation of a Partially Linear Regression in Triangular Systems

Author

Listed:
  • Xin Geng

    (IFPRI)

  • Carlos Martins-Filho

    (University of Colorado, Department of Economics)

  • Feng Yao

    (West Virginia University, Department of Economics)

Abstract

We propose a kernel-based estimator for a partially linear regression in a triangular system where endogenous regressors appear both in the nonparametric and linear components of the regression. Compared with alternative estimators currently available in the literature (Ai and Chen 2003; Otsu 2011), our estimator has an explicit functional form, is easier to implement, and exhibits better experimental finite sample performance. The estimator is inspired by the control function approach of Newey et al. (1999) and was initially proposed by Martins-Filho and Yao (2012). It explores conditional moment restrictions that make it suitable for additive regression estimation as in Kim et al. (1999) and Manzan and Zerom (2005). We establish consistency and asymptotic normality of the estimator for the parameters in the linear component of the model and give a uniform convergence rate for the estimator of the nonparametric component. In addition, for statistical inference, a consistent estimator for the covariance of the limiting distribution of the parametric estimator is provided. We illustrate the empirical viability of our estimation procedure by applying it to the study of the impact of foreign aid and policy on growth of per capita gross domestic product (GDP) in developing countries.

Suggested Citation

  • 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.
  • Handle: RePEc:wvu:wpaper:15-46
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    References listed on IDEAS

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    More about this item

    Keywords

    partially linear regression; endogeneity; semiparametric instrumental variable estimation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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