Network effects error components models

Autores
Montes Rojas, Gabriel Victorio
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This paper develops a random effects error components structure fornetwork data regression models. In particular, it allows for edge and triangle specific components, which serve as a basal model for modeling network effects. It then evaluates the potential effects of ignoring network effects in the estimation of the variance-covariance matrix. Network effects will typically imply heteroskedasticity, and as with the Moulton factor, the key role is given by the joint consideration of the intra-network correlation of the error term(s) and the covariates. Then it proposes consistent estimator of the variance components and Lagrange Multiplier tests for evaluating the appropriate model of random components in networks. Monte Carlo simulations show the tests have very good performance in finite samples.
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
LIII Conferencia Anual de la Asociación Argentina de Economía Política
La Plata
Argentina
Asociación Argentina de Economía Política
Materia
NETWORKS
RANDOM EFFECTS
Clusters
Moulton factor
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/155201
Acceder
id CONICETDig_eb36ca623f1272eda023f04df7ef29db
oai_identifier_str oai:ri.conicet.gov.ar:11336/155201
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Network effects error components modelsMontes Rojas, Gabriel VictorioNETWORKSRANDOM EFFECTSClustersMoulton factorhttps://purl.org/becyt/ford/5.2https://purl.org/becyt/ford/5This paper develops a random effects error components structure fornetwork data regression models. In particular, it allows for edge and triangle specific components, which serve as a basal model for modeling network effects. It then evaluates the potential effects of ignoring network effects in the estimation of the variance-covariance matrix. Network effects will typically imply heteroskedasticity, and as with the Moulton factor, the key role is given by the joint consideration of the intra-network correlation of the error term(s) and the covariates. Then it proposes consistent estimator of the variance components and Lagrange Multiplier tests for evaluating the appropriate model of random components in networks. Monte Carlo simulations show the tests have very good performance in finite samples.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; ArgentinaLIII Conferencia Anual de la Asociación Argentina de Economía PolíticaLa PlataArgentinaAsociación Argentina de Economía PolíticaAsociación Argentina de Economía Política2019info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectCongresoBookhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/155201Network effects error components models; LIII Conferencia Anual de la Asociación Argentina de Economía Política; La Plata; Argentina; 2018; 169-169CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://aaep.org.ar/anales/download/2018/LibroResumenes2018d.pdfinfo:eu-repo/semantics/altIdentifier/url/https://aaep.org.ar/site/reunion2018.htmlNacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:00:00Zoai:ri.conicet.gov.ar:11336/155201instacron: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 10:00:00.668CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Network effects error components models
title Network effects error components models
spellingShingle Network effects error components models
Montes Rojas, Gabriel Victorio
NETWORKS
RANDOM EFFECTS
Clusters
Moulton factor
title_short Network effects error components models
title_full Network effects error components models
title_fullStr Network effects error components models
title_full_unstemmed Network effects error components models
title_sort Network effects error components models
dc.creator.none.fl_str_mv Montes Rojas, Gabriel Victorio
author Montes Rojas, Gabriel Victorio
author_facet Montes Rojas, Gabriel Victorio
author_role author
dc.subject.none.fl_str_mv NETWORKS
RANDOM EFFECTS
Clusters
Moulton factor
topic NETWORKS
RANDOM EFFECTS
Clusters
Moulton factor
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 develops a random effects error components structure fornetwork data regression models. In particular, it allows for edge and triangle specific components, which serve as a basal model for modeling network effects. It then evaluates the potential effects of ignoring network effects in the estimation of the variance-covariance matrix. Network effects will typically imply heteroskedasticity, and as with the Moulton factor, the key role is given by the joint consideration of the intra-network correlation of the error term(s) and the covariates. Then it proposes consistent estimator of the variance components and Lagrange Multiplier tests for evaluating the appropriate model of random components in networks. Monte Carlo simulations show the tests have very good performance in finite samples.
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
LIII Conferencia Anual de la Asociación Argentina de Economía Política
La Plata
Argentina
Asociación Argentina de Economía Política
description This paper develops a random effects error components structure fornetwork data regression models. In particular, it allows for edge and triangle specific components, which serve as a basal model for modeling network effects. It then evaluates the potential effects of ignoring network effects in the estimation of the variance-covariance matrix. Network effects will typically imply heteroskedasticity, and as with the Moulton factor, the key role is given by the joint consideration of the intra-network correlation of the error term(s) and the covariates. Then it proposes consistent estimator of the variance components and Lagrange Multiplier tests for evaluating the appropriate model of random components in networks. Monte Carlo simulations show the tests have very good performance in finite samples.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Congreso
Book
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/155201
Network effects error components models; LIII Conferencia Anual de la Asociación Argentina de Economía Política; La Plata; Argentina; 2018; 169-169
CONICET Digital
CONICET
url http://hdl.handle.net/11336/155201
identifier_str_mv Network effects error components models; LIII Conferencia Anual de la Asociación Argentina de Economía Política; La Plata; Argentina; 2018; 169-169
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://aaep.org.ar/anales/download/2018/LibroResumenes2018d.pdf
info:eu-repo/semantics/altIdentifier/url/https://aaep.org.ar/site/reunion2018.html
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.coverage.none.fl_str_mv Nacional
dc.publisher.none.fl_str_mv Asociación Argentina de Economía Política
publisher.none.fl_str_mv Asociación Argentina de Economía Política
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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