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Nonparametric panel data regression with parametric cross-sectional dependence
[Regional adjustment and wage flexibility in the European Union]

Author

Listed:
  • Alexandra Soberon
  • Juan M Rodriguez-Poo
  • Peter M Robinson

Abstract

SummaryIn this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A generalised least squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterising the cross-sectional dependence, a local linear estimator is shown to be dominated by this type of GLS estimator. Also, possible gains in terms of rate of convergence are studied. Asymptotically optimal bandwidth choice is justified. To assess the finite sample performance of the proposed estimators, a Monte Carlo study is carried out. Further, some empirical applications are conducted with the aim of analysing the implications of the European Monetary Union for its member countries.

Suggested Citation

  • Alexandra Soberon & Juan M Rodriguez-Poo & Peter M Robinson, 2022. "Nonparametric panel data regression with parametric cross-sectional dependence [Regional adjustment and wage flexibility in the European Union]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 114-133.
    • Handle: RePEc:oup:emjrnl:v:25:y:2022:i:1:p:114-133.
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    File URL: http://hdl.handle.net/10.1093/ectj/utab016
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