Testing for serial correlation in hierarchical linear models

Autores
Alejo, Osvaldo Javier; Montes Rojas, Gabriel Victorio; Sosa Escudero, Walter
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper proposes a simple hierarchical model and a testing strategy to identify intra-cluster correlations, in the form of nested random effects and serially correlated error components. We focus on intra-cluster serial correlation at different nested levels, a topic that has not been studied in the literature before. A Neyman's C(α) framework is used to derive LM-type tests that allow researchers to identify the appropriate level of clustering as well as the type of intra-group correlation. An extensive Monte Carlo exercise shows that the proposed tests perform well in finite samples and under non-Gaussian distributions.
Fil: Alejo, Osvaldo Javier. Universidad Nacional de La Plata. Facultad de Ciencias Económicas. Departamento de Ciencias Económicas. Centro de Estudios Distributivos Laborales y Sociales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Montes Rojas, Gabriel Victorio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; Argentina. Universidad de Barcelona; España
Fil: Sosa Escudero, Walter. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
CLUSTERS
RANDOM EFFECTS
SERIAL CORRELATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/87244
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Testing for serial correlation in hierarchical linear modelsAlejo, Osvaldo JavierMontes Rojas, Gabriel VictorioSosa Escudero, WalterCLUSTERSRANDOM EFFECTSSERIAL CORRELATIONhttps://purl.org/becyt/ford/5.2https://purl.org/becyt/ford/5This paper proposes a simple hierarchical model and a testing strategy to identify intra-cluster correlations, in the form of nested random effects and serially correlated error components. We focus on intra-cluster serial correlation at different nested levels, a topic that has not been studied in the literature before. A Neyman's C(α) framework is used to derive LM-type tests that allow researchers to identify the appropriate level of clustering as well as the type of intra-group correlation. An extensive Monte Carlo exercise shows that the proposed tests perform well in finite samples and under non-Gaussian distributions.Fil: Alejo, Osvaldo Javier. Universidad Nacional de La Plata. Facultad de Ciencias Económicas. Departamento de Ciencias Económicas. Centro de Estudios Distributivos Laborales y Sociales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Montes Rojas, Gabriel Victorio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; Argentina. Universidad de Barcelona; EspañaFil: Sosa Escudero, Walter. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier2018-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/87244Alejo, Osvaldo Javier; Montes Rojas, Gabriel Victorio; Sosa Escudero, Walter; Testing for serial correlation in hierarchical linear models; Elsevier; Journal Of Multivariate Analysis; 165; 5-2018; 101-1160047-259XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0047259X17307285info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmva.2017.11.007info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:11Zoai:ri.conicet.gov.ar:11336/87244instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:33:12.249CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Testing for serial correlation in hierarchical linear models
title Testing for serial correlation in hierarchical linear models
spellingShingle Testing for serial correlation in hierarchical linear models
Alejo, Osvaldo Javier
CLUSTERS
RANDOM EFFECTS
SERIAL CORRELATION
title_short Testing for serial correlation in hierarchical linear models
title_full Testing for serial correlation in hierarchical linear models
title_fullStr Testing for serial correlation in hierarchical linear models
title_full_unstemmed Testing for serial correlation in hierarchical linear models
title_sort Testing for serial correlation in hierarchical linear models
dc.creator.none.fl_str_mv Alejo, Osvaldo Javier
Montes Rojas, Gabriel Victorio
Sosa Escudero, Walter
author Alejo, Osvaldo Javier
author_facet Alejo, Osvaldo Javier
Montes Rojas, Gabriel Victorio
Sosa Escudero, Walter
author_role author
author2 Montes Rojas, Gabriel Victorio
Sosa Escudero, Walter
author2_role author
author
dc.subject.none.fl_str_mv CLUSTERS
RANDOM EFFECTS
SERIAL CORRELATION
topic CLUSTERS
RANDOM EFFECTS
SERIAL CORRELATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.2
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv This paper proposes a simple hierarchical model and a testing strategy to identify intra-cluster correlations, in the form of nested random effects and serially correlated error components. We focus on intra-cluster serial correlation at different nested levels, a topic that has not been studied in the literature before. A Neyman's C(α) framework is used to derive LM-type tests that allow researchers to identify the appropriate level of clustering as well as the type of intra-group correlation. An extensive Monte Carlo exercise shows that the proposed tests perform well in finite samples and under non-Gaussian distributions.
Fil: Alejo, Osvaldo Javier. Universidad Nacional de La Plata. Facultad de Ciencias Económicas. Departamento de Ciencias Económicas. Centro de Estudios Distributivos Laborales y Sociales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Montes Rojas, Gabriel Victorio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Interdisciplinario de Economía Política de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires; Argentina. Universidad de Barcelona; España
Fil: Sosa Escudero, Walter. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description This paper proposes a simple hierarchical model and a testing strategy to identify intra-cluster correlations, in the form of nested random effects and serially correlated error components. We focus on intra-cluster serial correlation at different nested levels, a topic that has not been studied in the literature before. A Neyman's C(α) framework is used to derive LM-type tests that allow researchers to identify the appropriate level of clustering as well as the type of intra-group correlation. An extensive Monte Carlo exercise shows that the proposed tests perform well in finite samples and under non-Gaussian distributions.
publishDate 2018
dc.date.none.fl_str_mv 2018-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/87244
Alejo, Osvaldo Javier; Montes Rojas, Gabriel Victorio; Sosa Escudero, Walter; Testing for serial correlation in hierarchical linear models; Elsevier; Journal Of Multivariate Analysis; 165; 5-2018; 101-116
0047-259X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/87244
identifier_str_mv Alejo, Osvaldo Javier; Montes Rojas, Gabriel Victorio; Sosa Escudero, Walter; Testing for serial correlation in hierarchical linear models; Elsevier; Journal Of Multivariate Analysis; 165; 5-2018; 101-116
0047-259X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0047259X17307285
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmva.2017.11.007
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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