dc.contributor.author | Arteche González, Jesús María | |
dc.date.accessioned | 2024-05-07T16:14:13Z | |
dc.date.available | 2024-05-07T16:14:13Z | |
dc.date.issued | 2021-07-08 | |
dc.identifier.citation | Econometrics and Statistics 29 : 1-15 (2024) | es_ES |
dc.identifier.issn | 2468-0389 | |
dc.identifier.issn | 10.1016/j.ecosta.2021.06.002 | |
dc.identifier.uri | http://hdl.handle.net/10810/67649 | |
dc.description.abstract | Bootstrapping time series requires dealing with the dependence that may exist within
the sample. Several strategies have been proposed, but their validity has only been
proven for short memory series and there has been little progress in their theoretical
properties under long memory, where strong persistence may invalidate conventional
techniques. The first contribution is to review all these recent advances, paying particular
attention to those approaches that do not rely on parametric models and offering a
guide for practitioners who wish to use them in semiparametric or nonparametric contexts.
The second contribution is a Monte Carlo analysis of the applicability of these
bootstrap techniques for approximating the distribution of low frequency estimators of
the memory parameter based on spectral behaviour at frequencies close to the origin. | es_ES |
dc.description.sponsorship | Research supported by the Spanish Ministry of Science and Innovation and ERDF grants ECO2016-76884-P, PID2019-105183GB-I00 and UPV/EHU Econometrics Research Group (Basque Government grant IT1359-19). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIN/ECO2016-76884-P | es_ES |
dc.relation | info:eu-repo/grantAgreement/MCIN/PID2019-105183GB-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | long memory | es_ES |
dc.subject | bootstrap | es_ES |
dc.subject | memory parameter estimation | es_ES |
dc.title | Bootstrapping long memory time series: Application in low frequency estimators | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2021 The Author(s). Published by Elsevier B.V. on behalf of EcoSta Econometrics and Statistics. This is an open access article under the CC BY-NC-ND license | es_ES |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S2452306221000769 | es_ES |
dc.departamentoes | Métodos Cuantitativos | es_ES |
dc.departamentoeu | Metodo Kuantitatiboak | es_ES |