Global search regression: a new automatic model-selection technique for cross-section, time-series, and panel-data regressions

Autores
Gluzmann, Pablo Alfredo; Panigo, Demian Tupac
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this article, we present gsreg, a new automatic model-selection technique for cross-section, time-series, and panel-data regressions. Like other exhaustive search algorithms (for example, vselect), gsreg avoids characteristic path-dependence traps of standard approaches as well as backward- and forwardlooking approaches (like PcGets or relevant transformation of the inputs network approach). However, gsreg is the first code that 1) guarantees optimality with out-of-sample selection criteria; 2) allows residual testing for each alternative; and 3) provides (depending on user specifications) a full-information dataset with outcome statistics for every alternative model.
Facultad de Ciencias Económicas
Centro de Estudios Distributivos, Laborales y Sociales
Materia
Ciencias Económicas
st0383
gsreg
Automatic model selection
vselect
PcGets
RETINA
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/123907
Acceder
id SEDICI_b71625f4ecf7e2eda252e632d8b9bac5
oai_identifier_str oai:sedici.unlp.edu.ar:10915/123907
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Global search regression: a new automatic model-selection technique for cross-section, time-series, and panel-data regressionsGluzmann, Pablo AlfredoPanigo, Demian TupacCiencias Económicasst0383gsregAutomatic model selectionvselectPcGetsRETINAIn this article, we present gsreg, a new automatic model-selection technique for cross-section, time-series, and panel-data regressions. Like other exhaustive search algorithms (for example, vselect), gsreg avoids characteristic path-dependence traps of standard approaches as well as backward- and forwardlooking approaches (like PcGets or relevant transformation of the inputs network approach). However, gsreg is the first code that 1) guarantees optimality with out-of-sample selection criteria; 2) allows residual testing for each alternative; and 3) provides (depending on user specifications) a full-information dataset with outcome statistics for every alternative model.Facultad de Ciencias EconómicasCentro de Estudios Distributivos, Laborales y Sociales2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf325-349http://sedici.unlp.edu.ar/handle/10915/123907enginfo:eu-repo/semantics/altIdentifier/issn/1536-867Xinfo:eu-repo/semantics/altIdentifier/issn/1536-8734info:eu-repo/semantics/altIdentifier/doi/10.1177/1536867x1501500201info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:10:19Zoai:sedici.unlp.edu.ar:10915/123907Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:10:19.563SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Global search regression: a new automatic model-selection technique for cross-section, time-series, and panel-data regressions
title Global search regression: a new automatic model-selection technique for cross-section, time-series, and panel-data regressions
spellingShingle Global search regression: a new automatic model-selection technique for cross-section, time-series, and panel-data regressions
Gluzmann, Pablo Alfredo
Ciencias Económicas
st0383
gsreg
Automatic model selection
vselect
PcGets
RETINA
title_short Global search regression: a new automatic model-selection technique for cross-section, time-series, and panel-data regressions
title_full Global search regression: a new automatic model-selection technique for cross-section, time-series, and panel-data regressions
title_fullStr Global search regression: a new automatic model-selection technique for cross-section, time-series, and panel-data regressions
title_full_unstemmed Global search regression: a new automatic model-selection technique for cross-section, time-series, and panel-data regressions
title_sort Global search regression: a new automatic model-selection technique for cross-section, time-series, and panel-data regressions
dc.creator.none.fl_str_mv Gluzmann, Pablo Alfredo
Panigo, Demian Tupac
author Gluzmann, Pablo Alfredo
author_facet Gluzmann, Pablo Alfredo
Panigo, Demian Tupac
author_role author
author2 Panigo, Demian Tupac
author2_role author
dc.subject.none.fl_str_mv Ciencias Económicas
st0383
gsreg
Automatic model selection
vselect
PcGets
RETINA
topic Ciencias Económicas
st0383
gsreg
Automatic model selection
vselect
PcGets
RETINA
dc.description.none.fl_txt_mv In this article, we present gsreg, a new automatic model-selection technique for cross-section, time-series, and panel-data regressions. Like other exhaustive search algorithms (for example, vselect), gsreg avoids characteristic path-dependence traps of standard approaches as well as backward- and forwardlooking approaches (like PcGets or relevant transformation of the inputs network approach). However, gsreg is the first code that 1) guarantees optimality with out-of-sample selection criteria; 2) allows residual testing for each alternative; and 3) provides (depending on user specifications) a full-information dataset with outcome statistics for every alternative model.
Facultad de Ciencias Económicas
Centro de Estudios Distributivos, Laborales y Sociales
description In this article, we present gsreg, a new automatic model-selection technique for cross-section, time-series, and panel-data regressions. Like other exhaustive search algorithms (for example, vselect), gsreg avoids characteristic path-dependence traps of standard approaches as well as backward- and forwardlooking approaches (like PcGets or relevant transformation of the inputs network approach). However, gsreg is the first code that 1) guarantees optimality with out-of-sample selection criteria; 2) allows residual testing for each alternative; and 3) provides (depending on user specifications) a full-information dataset with outcome statistics for every alternative model.
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/123907
url http://sedici.unlp.edu.ar/handle/10915/123907
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1536-867X
info:eu-repo/semantics/altIdentifier/issn/1536-8734
info:eu-repo/semantics/altIdentifier/doi/10.1177/1536867x1501500201
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
325-349
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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