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Automatic selection of indicators in a fully saturated regression

Bibliographic Details
Main Author: Santos, Carlos
Publication Date: 2008
Other Authors: Hendry, David F., Johansen, Soren
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.14/6624
Summary: We consider selecting a regression model, using a variant of the generalto- specific algorithm in PcGets, when there are more variables than observations. We look at the special case where the variables are single impulse dummies, one defined for each observation. We show that this setting is unproblematic if tackled appropriately, and obtain the asymptotic distribution of the mean and variance in a location-scale model, under the null that no impulses matter. Monte Carlo simulations confirm the null distributions and suggest extensions to highly non-normal cases
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spelling Automatic selection of indicators in a fully saturated regressionIndicatorsRegression saturationSubset selectionModel selectionWe consider selecting a regression model, using a variant of the generalto- specific algorithm in PcGets, when there are more variables than observations. We look at the special case where the variables are single impulse dummies, one defined for each observation. We show that this setting is unproblematic if tackled appropriately, and obtain the asymptotic distribution of the mean and variance in a location-scale model, under the null that no impulses matter. Monte Carlo simulations confirm the null distributions and suggest extensions to highly non-normal casesSpringerVeritatiSantos, CarlosHendry, David F.Johansen, Soren2011-10-21T10:22:49Z20082008-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.14/6624eng10.1007/s00180-007-0054-zinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-13T15:08:00Zoai:repositorio.ucp.pt:10400.14/6624Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T02:10:25.674973Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Automatic selection of indicators in a fully saturated regression
title Automatic selection of indicators in a fully saturated regression
spellingShingle Automatic selection of indicators in a fully saturated regression
Santos, Carlos
Indicators
Regression saturation
Subset selection
Model selection
title_short Automatic selection of indicators in a fully saturated regression
title_full Automatic selection of indicators in a fully saturated regression
title_fullStr Automatic selection of indicators in a fully saturated regression
title_full_unstemmed Automatic selection of indicators in a fully saturated regression
title_sort Automatic selection of indicators in a fully saturated regression
author Santos, Carlos
author_facet Santos, Carlos
Hendry, David F.
Johansen, Soren
author_role author
author2 Hendry, David F.
Johansen, Soren
author2_role author
author
dc.contributor.none.fl_str_mv Veritati
dc.contributor.author.fl_str_mv Santos, Carlos
Hendry, David F.
Johansen, Soren
dc.subject.por.fl_str_mv Indicators
Regression saturation
Subset selection
Model selection
topic Indicators
Regression saturation
Subset selection
Model selection
description We consider selecting a regression model, using a variant of the generalto- specific algorithm in PcGets, when there are more variables than observations. We look at the special case where the variables are single impulse dummies, one defined for each observation. We show that this setting is unproblematic if tackled appropriately, and obtain the asymptotic distribution of the mean and variance in a location-scale model, under the null that no impulses matter. Monte Carlo simulations confirm the null distributions and suggest extensions to highly non-normal cases
publishDate 2008
dc.date.none.fl_str_mv 2008
2008-01-01T00:00:00Z
2011-10-21T10:22:49Z
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/10400.14/6624
url http://hdl.handle.net/10400.14/6624
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1007/s00180-007-0054-z
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 Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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