Nonparametric panel data regression with parametric cross-sectional dependence

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URI: http://hdl.handle.net/10902/23862
ISSN: 1368-4221
ISSN: 1368-423X
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Autoría
Soberón Velez, Alexandra PilarAutoridad Unican; Rodríguez-Poo, Juan M.Autoridad Unican; Robinson, Peter M.
Fecha
2022
Derechos
© Royal Economic Society. Published by Oxford University Press. This is a pre-copyedited, author-produced version of an article accepted for publication in The Econometrics Journal following peer review. The version of record "Nonparametric panel data regression with parametric cross-sectional dependence Volume 25, Issue 1, January 2022, Pages 114-133", is available online at: https://academic.oup.com/ectj/article/25/1/114/6272425, https://doi.org/10.1093/ectj/utab016
Publicado en
The Econometrics Journal, Volume 25, Issue 1, January 2022, Pages 114-133,
Editorial
Oxford University Press
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Palabras clave
Local linear estimation
Panel data
Cross-sectional dependence
Generalized least squares
Optimal bandwidth
Pseudo maximum likelihood estimation
Resumen/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.
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