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Local GMM Estimation of Semiparametric Panel Data with Smooth Coefficient Models

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  • Kien Tran
  • Efthymios Tsionas

Abstract

In this article, we consider the estimation of semiparametric panel data smooth coefficient models. We propose a class of local generalized method of moments (LGMM) estimators that are simple and easy to implement in practice. We show that the proposed LGMM estimators are consistent and asymptotically normal. Monte Carlo simulations suggest that our proposed estimator performs quite well in finite samples. An empirical application using a large panel of U.K. firms is also presented.

Suggested Citation

  • Kien Tran & Efthymios Tsionas, 2010. "Local GMM Estimation of Semiparametric Panel Data with Smooth Coefficient Models," Econometric Reviews, Taylor & Francis Journals, vol. 29(1), pages 39-61.
    • Handle: RePEc:taf:emetrv:v:29:y:2010:i:1:p:39-61
    DOI: 10.1080/07474930903327856
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    References listed on IDEAS

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    7. Francesco Bravo, 2016. "Local Information Theoretic Methods for smooth Coefficients Dynamic Panel Data Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 690-708, September.
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