Panel data in accounting and finance: theory and application
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Tipo de documento: | Artigo |
Idioma: | eng por |
Título da fonte: | BBR. Brazilian Business Review (English edition. Online) |
Texto Completo: | http://www.bbronline.com.br/index.php/bbr/article/view/236 |
Resumo: | The use of models that involve longitudinal data in accounting and finance is common. However, there is often a lack of proper care regarding the criteria for adopting one model over another as well as an insufficiently detailed discussion of the possible estimators to be studied in each situation. This article presents, in conceptual and applied form, the main panel data estimators that can be used in these areas of knowledge and discusses the definition of the most consistent model to be adopted in function of the data characteristics. The models covered for short panels are the POLS with clustered robust standard errors, with between estimator, fixed effects, fixed effects with clustered robust standard errors, random effects and random effects with clustered robust standard errors. In turn, for long panels, the models discussed are fixed effects, random effects, fixed effects with AR(1) error terms, random effects with AR(1) error terms, POLS with AR(1) errors and pooled FGLS with AR(1) errors. The models are also applied to a real case, based on data from Compustat Global. At the end, the main routines for applying each of the models in Stata are presented. |
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Panel data in accounting and finance: theory and applicationDados em painel em contabilidade e finanças: teoria e aplicaçãoPanel dataaccountingfinanceestimation methodsDados em painelcontabilidadefinançasmétodo de estimaçãoThe use of models that involve longitudinal data in accounting and finance is common. However, there is often a lack of proper care regarding the criteria for adopting one model over another as well as an insufficiently detailed discussion of the possible estimators to be studied in each situation. This article presents, in conceptual and applied form, the main panel data estimators that can be used in these areas of knowledge and discusses the definition of the most consistent model to be adopted in function of the data characteristics. The models covered for short panels are the POLS with clustered robust standard errors, with between estimator, fixed effects, fixed effects with clustered robust standard errors, random effects and random effects with clustered robust standard errors. In turn, for long panels, the models discussed are fixed effects, random effects, fixed effects with AR(1) error terms, random effects with AR(1) error terms, POLS with AR(1) errors and pooled FGLS with AR(1) errors. The models are also applied to a real case, based on data from Compustat Global. At the end, the main routines for applying each of the models in Stata are presented.A utilização de modelos que envolvam dados longitudinais em contabilidade e finanças tem sido recorrente. No entanto verifica-se uma falta de cuidado quanto aos critérios para a adoção de um modelo em detrimento de outro, bem como a ausência de uma discussão mais detalhada sobre os possíveis estimadores a serem estudados em cada situação. Este artigo tem por objetivo apresentar, de forma conceitual e aplicada, os principais estimadores de dados em painel que podem ser utilizados nessas áreas do conhecimento, bem como auxiliar na definição do modelo mais consistente a ser adotado, em função das características dos dados. Para um painel curto, são discutidos os modelos POLS com erros-padrão robustos clusterizados, com estimadorbetween, efeitos fixos, efeitos fixos com erros-padrão robustos clusterizados, efeitos aleatórios e efeitos aleatórios com erros-padrão robustos clusterizados. Já para um painel longo, são discutidos os modelos com efeitos fixos, efeitos aleatórios, efeitos fixos com termos de erro AR(1), efeitos aleatórios com termos de erro AR(1), POLS com erros AR(1) e Pooled FGLS com erros AR(1). Este artigo também tem por propósito aplicar tais modelos em um caso real, com base nos dados da Compustat Global. Ao final, são apresentadas as principais rotinas para a aplicação de cada um dos modelos em Stata.FUCAPE Business Shool2013-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed ArticleArtigo revisado pelos paresapplication/pdfapplication/pdfhttp://www.bbronline.com.br/index.php/bbr/article/view/23610.15728/bbr.2013.10.1.6Brazilian Business Review; Vol. 10 No. 1 (2013): January to March 2013; 127-149Brazilian Business Review; v. 10 n. 1 (2013): Janeiro a Março de 2013; 127-1491808-23861807-734Xreponame:BBR. Brazilian Business Review (English edition. Online)instname:Fucape Business School (FBS)instacron:FBSengporhttp://www.bbronline.com.br/index.php/bbr/article/view/236/357http://www.bbronline.com.br/index.php/bbr/article/view/236/358Fávero, Luiz Paulo Lopesinfo:eu-repo/semantics/openAccess2018-11-06T19:54:02Zoai:ojs.pkp.sfu.ca:article/236Revistahttps://www.bbronline.com.br/index.php/bbr/indexONGhttp://www.bbronline.com.br/index.php/bbr/oai|| bbronline@bbronline.com.br1808-23861808-2386opendoar:2018-11-06T19:54:02BBR. Brazilian Business Review (English edition. Online) - Fucape Business School (FBS)false |
dc.title.none.fl_str_mv |
Panel data in accounting and finance: theory and application Dados em painel em contabilidade e finanças: teoria e aplicação |
title |
Panel data in accounting and finance: theory and application |
spellingShingle |
Panel data in accounting and finance: theory and application Fávero, Luiz Paulo Lopes Panel data accounting finance estimation methods Dados em painel contabilidade finanças método de estimação |
title_short |
Panel data in accounting and finance: theory and application |
title_full |
Panel data in accounting and finance: theory and application |
title_fullStr |
Panel data in accounting and finance: theory and application |
title_full_unstemmed |
Panel data in accounting and finance: theory and application |
title_sort |
Panel data in accounting and finance: theory and application |
author |
Fávero, Luiz Paulo Lopes |
author_facet |
Fávero, Luiz Paulo Lopes |
author_role |
author |
dc.contributor.author.fl_str_mv |
Fávero, Luiz Paulo Lopes |
dc.subject.por.fl_str_mv |
Panel data accounting finance estimation methods Dados em painel contabilidade finanças método de estimação |
topic |
Panel data accounting finance estimation methods Dados em painel contabilidade finanças método de estimação |
description |
The use of models that involve longitudinal data in accounting and finance is common. However, there is often a lack of proper care regarding the criteria for adopting one model over another as well as an insufficiently detailed discussion of the possible estimators to be studied in each situation. This article presents, in conceptual and applied form, the main panel data estimators that can be used in these areas of knowledge and discusses the definition of the most consistent model to be adopted in function of the data characteristics. The models covered for short panels are the POLS with clustered robust standard errors, with between estimator, fixed effects, fixed effects with clustered robust standard errors, random effects and random effects with clustered robust standard errors. In turn, for long panels, the models discussed are fixed effects, random effects, fixed effects with AR(1) error terms, random effects with AR(1) error terms, POLS with AR(1) errors and pooled FGLS with AR(1) errors. The models are also applied to a real case, based on data from Compustat Global. At the end, the main routines for applying each of the models in Stata are presented. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article Artigo revisado pelos pares |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.bbronline.com.br/index.php/bbr/article/view/236 10.15728/bbr.2013.10.1.6 |
url |
http://www.bbronline.com.br/index.php/bbr/article/view/236 |
identifier_str_mv |
10.15728/bbr.2013.10.1.6 |
dc.language.iso.fl_str_mv |
eng por |
language |
eng por |
dc.relation.none.fl_str_mv |
http://www.bbronline.com.br/index.php/bbr/article/view/236/357 http://www.bbronline.com.br/index.php/bbr/article/view/236/358 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
FUCAPE Business Shool |
publisher.none.fl_str_mv |
FUCAPE Business Shool |
dc.source.none.fl_str_mv |
Brazilian Business Review; Vol. 10 No. 1 (2013): January to March 2013; 127-149 Brazilian Business Review; v. 10 n. 1 (2013): Janeiro a Março de 2013; 127-149 1808-2386 1807-734X reponame:BBR. Brazilian Business Review (English edition. Online) instname:Fucape Business School (FBS) instacron:FBS |
instname_str |
Fucape Business School (FBS) |
instacron_str |
FBS |
institution |
FBS |
reponame_str |
BBR. Brazilian Business Review (English edition. Online) |
collection |
BBR. Brazilian Business Review (English edition. Online) |
repository.name.fl_str_mv |
BBR. Brazilian Business Review (English edition. Online) - Fucape Business School (FBS) |
repository.mail.fl_str_mv |
|| bbronline@bbronline.com.br |
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1754732237879771136 |