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Nonparametric Estimation and Inference for Panel Data Models

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  • Christopher F. Parmeter
  • Jeffrey S. Racine

Abstract

This chapter surveys nonparametric methods for estimation and inference in a panel data setting. Methods surveyed include profile likelihood, kernel smoothers, as well as series and sieve estimators. The practical application of nonparametric panel-based techniques is less prevalent that, say, nonparametric density and regression techniques. It is our hope that the material covered in this chapter will prove useful and facilitate their adoption by practitioners.

Suggested Citation

  • Christopher F. Parmeter & Jeffrey S. Racine, 2018. "Nonparametric Estimation and Inference for Panel Data Models," Department of Economics Working Papers 2018-02, McMaster University.
    • Handle: RePEc:mcm:deptwp:2018-02
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    File URL: http://socialsciences.mcmaster.ca/econ/rsrch/papers/archive/2018-02.pdf
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    References listed on IDEAS

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    3. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2021. "Interactive R&D Spillovers: an estimation strategy based on forecasting-driven model selection," Working Papers hal-03224910, HAL.
    4. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," SEEDS Working Papers 0120, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jan 2020.
    5. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2019. "Nonparametric estimation of R&D international spillovers," Post-Print hal-02789474, HAL.
    6. Taining Wang & Jinjing Tian, 2020. "Recasting the trade impact on labor share: a fixed-effect semiparametric estimation study," Empirical Economics, Springer, vol. 58(5), pages 2465-2511, May.
    7. Ivan Korolev, 2019. "A Consistent LM Type Specification Test for Semiparametric Panel Data Models," Papers 1909.05649, arXiv.org.
    8. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2021. "Interactive R&D Spillovers: An estimation strategy based on forecasting-driven model selection," SEEDS Working Papers 0621, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jun 2021.

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