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Nonparametric panel data regression with parametric cross-sectional dependence

Soberon, Alexandra, Rodriguez-Poo, Juan M. and Robinson, Peter M. (2022) Nonparametric panel data regression with parametric cross-sectional dependence. Econometrics Journal, 25 (1). 114 - 133. ISSN 1368-4221

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Identification Number: 10.1093/ectj/utab016

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.

Item Type: Article
Official URL: https://academic.oup.com/ectj
Additional Information: © 2021 Royal Economic Society.
Divisions: Economics
Subjects: H Social Sciences > HB Economic Theory
JEL classification: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods
C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C23 - Models with Panel Data
Date Deposited: 06 Jan 2023 15:36
Last Modified: 31 May 2024 18:12
URI: http://eprints.lse.ac.uk/id/eprint/117785

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