A new regression-based tail index estimator

Detalhes bibliográficos
Autor(a) principal: Nicolau, João
Data de Publicação: 2019
Outros Autores: Rodrigues, Paulo M. M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.5/27504
Resumo: A new regression-based approach for the estimation of the tail index of heavy-tailed distributions with several important properties is introduced. First, it provides a bias reduction when compared to available regression-based methods; second, it is resilient to the choice of the tail length used for the estimation of the tail index; third, when the effect of the slowly varying function at infinity of the Pareto distribution vanishes slowly, it continues to perform satisfactorily; and fourth, it performs well under dependence of unknown form. An approach to compute the asymptotic variance under time dependence and conditional heteroskcedasticity is also provided.
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spelling A new regression-based tail index estimatorRegression-based ApproachPareto-type ModelMonte Carlo SimulationHeteroskcedasticityA new regression-based approach for the estimation of the tail index of heavy-tailed distributions with several important properties is introduced. First, it provides a bias reduction when compared to available regression-based methods; second, it is resilient to the choice of the tail length used for the estimation of the tail index; third, when the effect of the slowly varying function at infinity of the Pareto distribution vanishes slowly, it continues to perform satisfactorily; and fourth, it performs well under dependence of unknown form. An approach to compute the asymptotic variance under time dependence and conditional heteroskcedasticity is also provided.Harvard College and the Massachusetts Institute of TechnologyRepositório da Universidade de LisboaNicolau, JoãoRodrigues, Paulo M. M.2023-03-24T10:18:26Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/27504engNicolau, João and Paulo M. M. Rodrigues .(2019). “A new regression-based tail index estimator”. Review of Economics and Statistics, Vol. 101. No. 4: pp. 667-680. (Search PDF in 2023).10.1162/rest_a_00768info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-04-02T01:34:39Zoai:www.repository.utl.pt:10400.5/27504Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:48:21.372979Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A new regression-based tail index estimator
title A new regression-based tail index estimator
spellingShingle A new regression-based tail index estimator
Nicolau, João
Regression-based Approach
Pareto-type Model
Monte Carlo Simulation
Heteroskcedasticity
title_short A new regression-based tail index estimator
title_full A new regression-based tail index estimator
title_fullStr A new regression-based tail index estimator
title_full_unstemmed A new regression-based tail index estimator
title_sort A new regression-based tail index estimator
author Nicolau, João
author_facet Nicolau, João
Rodrigues, Paulo M. M.
author_role author
author2 Rodrigues, Paulo M. M.
author2_role author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Nicolau, João
Rodrigues, Paulo M. M.
dc.subject.por.fl_str_mv Regression-based Approach
Pareto-type Model
Monte Carlo Simulation
Heteroskcedasticity
topic Regression-based Approach
Pareto-type Model
Monte Carlo Simulation
Heteroskcedasticity
description A new regression-based approach for the estimation of the tail index of heavy-tailed distributions with several important properties is introduced. First, it provides a bias reduction when compared to available regression-based methods; second, it is resilient to the choice of the tail length used for the estimation of the tail index; third, when the effect of the slowly varying function at infinity of the Pareto distribution vanishes slowly, it continues to perform satisfactorily; and fourth, it performs well under dependence of unknown form. An approach to compute the asymptotic variance under time dependence and conditional heteroskcedasticity is also provided.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-01-01T00:00:00Z
2023-03-24T10:18:26Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.5/27504
url http://hdl.handle.net/10400.5/27504
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Nicolau, João and Paulo M. M. Rodrigues .(2019). “A new regression-based tail index estimator”. Review of Economics and Statistics, Vol. 101. No. 4: pp. 667-680. (Search PDF in 2023).
10.1162/rest_a_00768
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Harvard College and the Massachusetts Institute of Technology
publisher.none.fl_str_mv Harvard College and the Massachusetts Institute of Technology
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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