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
Full text not available from this repository.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: | 12 Dec 2024 03:30 |
| URI: | http://eprints.lse.ac.uk/id/eprint/117785 |
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