Locally robust inference for non-Gaussian linear simultaneous equations models

All parameters in linear simultaneous equations models can be identified (up to permutation and sign) if the underlying structural shocks are independent and at most one of them is Gaussian. Unfortunately, existing inference methods that exploit such identifying assumptions suffer from size distorti...

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Detalles Bibliográficos
Autores: Lee, Adam, Mesters, Geert
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/58722
Acceso en línea:http://hdl.handle.net/10230/58722
http://dx.doi.org/10.1016/j.jeconom.2023.105647
Access Level:acceso abierto
Palabra clave:Weak identification
Semiparametric modeling
Independent component analysis
Simultaneous equations
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spelling Locally robust inference for non-Gaussian linear simultaneous equations modelsLee, AdamMesters, GeertWeak identificationSemiparametric modelingIndependent component analysisSimultaneous equationsAll parameters in linear simultaneous equations models can be identified (up to permutation and sign) if the underlying structural shocks are independent and at most one of them is Gaussian. Unfortunately, existing inference methods that exploit such identifying assumptions suffer from size distortions when the true distributions of the shocks are close to Gaussian. To address this weak non-Gaussian problem we develop a locally robust semi-parametric inference method which is simple to implement, improves coverage and retains good power properties. The finite sample properties of the methodology are illustrated in a large simulation study and an empirical study for the returns to schooling.Mesters acknowledges support from the Spanish Ministry of Economy and Competitiveness through the Ramon y Cajal fellowship (RYC2019-028287-I) and the Spanish Ministry of Economy and Competitiveness through the Severo Ochoa Programme for Centres of Excellence in R&D (CEX2019-000915-S) and The Netherlands Organization for Scientific Research (NWO) through the VENI research grant (016.Veni.195.036).Elsevier202420242024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/58722http://dx.doi.org/10.1016/j.jeconom.2023.105647reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésJournal of Econometrics. 2024;240(1):105647.info:eu-repo/grantAgreement/ES/2PE/CEX2019-000915-Sinfo:eu-repo/grantAgreement/ES/2PE/RYC2019-028287-I© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/587222026-06-12T07:21:37Z
dc.title.none.fl_str_mv Locally robust inference for non-Gaussian linear simultaneous equations models
title Locally robust inference for non-Gaussian linear simultaneous equations models
spellingShingle Locally robust inference for non-Gaussian linear simultaneous equations models
Lee, Adam
Weak identification
Semiparametric modeling
Independent component analysis
Simultaneous equations
title_short Locally robust inference for non-Gaussian linear simultaneous equations models
title_full Locally robust inference for non-Gaussian linear simultaneous equations models
title_fullStr Locally robust inference for non-Gaussian linear simultaneous equations models
title_full_unstemmed Locally robust inference for non-Gaussian linear simultaneous equations models
title_sort Locally robust inference for non-Gaussian linear simultaneous equations models
dc.creator.none.fl_str_mv Lee, Adam
Mesters, Geert
author Lee, Adam
author_facet Lee, Adam
Mesters, Geert
author_role author
author2 Mesters, Geert
author2_role author
dc.subject.none.fl_str_mv Weak identification
Semiparametric modeling
Independent component analysis
Simultaneous equations
topic Weak identification
Semiparametric modeling
Independent component analysis
Simultaneous equations
description All parameters in linear simultaneous equations models can be identified (up to permutation and sign) if the underlying structural shocks are independent and at most one of them is Gaussian. Unfortunately, existing inference methods that exploit such identifying assumptions suffer from size distortions when the true distributions of the shocks are close to Gaussian. To address this weak non-Gaussian problem we develop a locally robust semi-parametric inference method which is simple to implement, improves coverage and retains good power properties. The finite sample properties of the methodology are illustrated in a large simulation study and an empirical study for the returns to schooling.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/58722
http://dx.doi.org/10.1016/j.jeconom.2023.105647
url http://hdl.handle.net/10230/58722
http://dx.doi.org/10.1016/j.jeconom.2023.105647
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Journal of Econometrics. 2024;240(1):105647.
info:eu-repo/grantAgreement/ES/2PE/CEX2019-000915-S
info:eu-repo/grantAgreement/ES/2PE/RYC2019-028287-I
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
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repository.mail.fl_str_mv
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