| dc.contributor.author | Soberón Velez, Alexandra Pilar | |
| dc.contributor.author | Rodríguez-Poo, Juan M. | |
| dc.contributor.author | Robinson, Peter M. | |
| dc.contributor.other | Universidad de Cantabria | es_ES |
| dc.date.accessioned | 2022-02-04T10:22:49Z | |
| dc.date.available | 2024-02-01T00:45:36Z | |
| dc.date.issued | 2022 | |
| dc.identifier.issn | 1368-4221 | |
| dc.identifier.issn | 1368-423X | |
| dc.identifier.other | PID2019-105986GB-C22 | es_ES |
| dc.identifier.other | Research Project APIE 1/2015-17 | es_ES |
| dc.identifier.uri | http://hdl.handle.net/10902/23862 | |
| dc.description.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. | es_ES |
| dc.description.sponsorship | The authors gratefully acknowledge financial support from the Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del sistema de I+D+i y del Programa Estatal de I+D+i Orientada a los Retos de la Sociedad/Spanish Ministry of Science and Innovation. Ref. PID2019-105986GB-C22. In addition, this work is part of the Research Project APIE 1/2015-17: \New methods for the empirical análisis of financial markets" of the Santander Financial Institute (SANFI) of UCEIF Foundation resolved by the University of Cantabria and funded with sponsorship from Banco Santander. | es_ES |
| dc.format.extent | 21 p. | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | Oxford University Press | es_ES |
| dc.rights | © 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 | es_ES |
| dc.source | The Econometrics Journal, Volume 25, Issue 1, January 2022, Pages 114-133, | es_ES |
| dc.subject.other | Local linear estimation | es_ES |
| dc.subject.other | Panel data | es_ES |
| dc.subject.other | Cross-sectional dependence | es_ES |
| dc.subject.other | Generalized least squares | es_ES |
| dc.subject.other | Optimal bandwidth | es_ES |
| dc.subject.other | Pseudo maximum likelihood estimation | es_ES |
| dc.title | Nonparametric panel data regression with parametric cross-sectional dependence | es_ES |
| dc.type | info:eu-repo/semantics/article | es_ES |
| dc.relation.publisherVersion | https://doi.org/10.1093/ectj/utab016 | es_ES |
| dc.rights.accessRights | openAccess | es_ES |
| dc.identifier.DOI | 10.1093/ectj/utab016 | |
| dc.type.version | acceptedVersion | es_ES |
