Local Whittle estimation of long memory: Standard versus bias-reducing techniques

[EN] Frequency domain semiparametric estimation of memory parameters belongs to the standard toolkit of applied time series researchers. These methods are based on a local approximation of the spectral density, which robustifies the estimation methods against misspecification, but induces a loss wit...

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Detalles Bibliográficos
Autores: García Enríquez, Javier, Hualde Bilbao, Javier
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/64358
Acceso en línea:http://hdl.handle.net/10810/64358
Access Level:acceso abierto
Palabra clave:memory parameters
semiparametric estimation
bias-reducing techniques
fractionally integrated processes
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spelling Local Whittle estimation of long memory: Standard versus bias-reducing techniquesGarcía Enríquez, JavierHualde Bilbao, Javiermemory parameterssemiparametric estimationbias-reducing techniquesfractionally integrated processes[EN] Frequency domain semiparametric estimation of memory parameters belongs to the standard toolkit of applied time series researchers. These methods are based on a local approximation of the spectral density, which robustifies the estimation methods against misspecification, but induces a loss with respect to the parametric setting, where the spectral density is known up to a finite number of unknown parameters. In particular, standard semiparametric estimators have convergence rates no better than T^2/5 , whereas the rate T^1/2 is achievable under parametric assumptions. Refinements of the local approximation have been developed by means of bias-reducing techniques, implying that rates arbitrarily close to the parametric one are achievable in the semiparametric setting. Two of these approaches to cover more general settings (including non-stationarity) are extended. A Monte Carlo experiment of finite sample performance is used to assess whether the asymptotic advantages of the bias-reducing methods materialize in better finite sample behavior.Research supported by the Spanish Ministry of Science and Innovation grant ECO2015-64330-P and by the Spanish Ministry of Science and Innovation ERDF grant ECO2016-76884-P .Elsevier202420242019info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/64358reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.sciencedirect.com/science/article/pii/S2452306219300280?via%3Dihubinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/© 2019 EcoSta Econometrics and Statistics. Published by Elsevier B.V. under CC BY-NC-ND( https://creativecommons.org/licenses/by-nc-nd/4.0/)oai:addi.ehu.eus:10810/643582026-06-18T09:23:17Z
dc.title.none.fl_str_mv Local Whittle estimation of long memory: Standard versus bias-reducing techniques
title Local Whittle estimation of long memory: Standard versus bias-reducing techniques
spellingShingle Local Whittle estimation of long memory: Standard versus bias-reducing techniques
García Enríquez, Javier
memory parameters
semiparametric estimation
bias-reducing techniques
fractionally integrated processes
title_short Local Whittle estimation of long memory: Standard versus bias-reducing techniques
title_full Local Whittle estimation of long memory: Standard versus bias-reducing techniques
title_fullStr Local Whittle estimation of long memory: Standard versus bias-reducing techniques
title_full_unstemmed Local Whittle estimation of long memory: Standard versus bias-reducing techniques
title_sort Local Whittle estimation of long memory: Standard versus bias-reducing techniques
dc.creator.none.fl_str_mv García Enríquez, Javier
Hualde Bilbao, Javier
author García Enríquez, Javier
author_facet García Enríquez, Javier
Hualde Bilbao, Javier
author_role author
author2 Hualde Bilbao, Javier
author2_role author
dc.subject.none.fl_str_mv memory parameters
semiparametric estimation
bias-reducing techniques
fractionally integrated processes
topic memory parameters
semiparametric estimation
bias-reducing techniques
fractionally integrated processes
description [EN] Frequency domain semiparametric estimation of memory parameters belongs to the standard toolkit of applied time series researchers. These methods are based on a local approximation of the spectral density, which robustifies the estimation methods against misspecification, but induces a loss with respect to the parametric setting, where the spectral density is known up to a finite number of unknown parameters. In particular, standard semiparametric estimators have convergence rates no better than T^2/5 , whereas the rate T^1/2 is achievable under parametric assumptions. Refinements of the local approximation have been developed by means of bias-reducing techniques, implying that rates arbitrarily close to the parametric one are achievable in the semiparametric setting. Two of these approaches to cover more general settings (including non-stationarity) are extended. A Monte Carlo experiment of finite sample performance is used to assess whether the asymptotic advantages of the bias-reducing methods materialize in better finite sample behavior.
publishDate 2019
dc.date.none.fl_str_mv 2019
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/64358
url http://hdl.handle.net/10810/64358
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S2452306219300280?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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