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Nonparametric panel data regression with parametric cross-sectional dependence

Abstract: In 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.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Autoría: Soberon A., Rodriguez-Poo J.M., Robinson P.M.,

 Fuente: The Econometrics Journal, Volume 25, Issue 1, January 2022, Pages 114-133,

Editorial: Oxford University Press

 Año de publicación: 2022

Nº de páginas: 21

Tipo de publicación: Artículo de Revista

 DOI: 10.1093/ectj/utab016

ISSN: 1368-4221,1368-423X

Proyecto español: PID2019-105986GB-C22

Url de la publicación: https://doi.org/10.1093/ectj/utab016