Robust semi-parametric inference for two-stage production models: a beta regression approach
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/85512 |
Resumo: | The data envelopment analysis is related to a non-parametric mathematical tool used to assess the relative efficiency of productive units. In different studies on productive efficiency, it is common to employ semi-parametric procedures in two stages to determine whether any exogenous factors of interest affect the performance of productive units. However, some of these procedures, particularly those based on conventional statistical inference, generate inconsistent estimates when dealing with incoherent data-generating processes. This inconsistency arises due to the efficiency scores being limited to the unit interval, and the estimated scores often exhibit serial correlation and have limited observations. To address such inconsistency, several strategies have been suggested, with the most well-known being an algorithm based on a parametric bootstrap procedure using the truncated normal distribution and its regression model. In this work, we present a modification of this algorithm that utilizes the beta distribution and its regression structure. The beta model allows for better accommodation of asymmetry in the data distribution. Our proposed algorithm introduces inferential characteristics that are superior to the original algorithm, resulting in a more statistically coherent data-generating process and improving the consistency property. We have conducted computational experiments that demonstrate the improved results achieved by our proposal. |
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Robust semi-parametric inference for two-stage production models: a beta regression approachAsymmetryBootstrappingData envelopment analysisDecision-making unitsEfficiencyOptimization methodsSimar and Wilson algorithmStatistical consistencyR softwareCiências Naturais::MatemáticasParcerias para a implementação dos objetivosThe data envelopment analysis is related to a non-parametric mathematical tool used to assess the relative efficiency of productive units. In different studies on productive efficiency, it is common to employ semi-parametric procedures in two stages to determine whether any exogenous factors of interest affect the performance of productive units. However, some of these procedures, particularly those based on conventional statistical inference, generate inconsistent estimates when dealing with incoherent data-generating processes. This inconsistency arises due to the efficiency scores being limited to the unit interval, and the estimated scores often exhibit serial correlation and have limited observations. To address such inconsistency, several strategies have been suggested, with the most well-known being an algorithm based on a parametric bootstrap procedure using the truncated normal distribution and its regression model. In this work, we present a modification of this algorithm that utilizes the beta distribution and its regression structure. The beta model allows for better accommodation of asymmetry in the data distribution. Our proposed algorithm introduces inferential characteristics that are superior to the original algorithm, resulting in a more statistically coherent data-generating process and improving the consistency property. We have conducted computational experiments that demonstrate the improved results achieved by our proposal.ANCD -Agenția Națională pentru Cercetare și Dezvoltare(1200525)MDPIUniversidade do MinhoOspina, RaydonalBaltazar, Samuel G. F.Leiva, VíctorFigueroa-Zúñiga, JorgeCastro, Cecília2023-072023-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/85512engOspina, R.; Baltazar, S.G.F.; Leiva, V.; Figueroa-Zúñiga, J.; Castro, C. Robust Semi-Parametric Inference for Two-Stage Production Models: A Beta Regression Approach. Symmetry 2023, 15, 1362. https://doi.org/10.3390/sym150713622073-899410.3390/sym15071362https://www.mdpi.com/2073-8994/15/7/1362info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-10-21T01:23:58Zoai:repositorium.sdum.uminho.pt:1822/85512Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:05:19.300465Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Robust semi-parametric inference for two-stage production models: a beta regression approach |
title |
Robust semi-parametric inference for two-stage production models: a beta regression approach |
spellingShingle |
Robust semi-parametric inference for two-stage production models: a beta regression approach Ospina, Raydonal Asymmetry Bootstrapping Data envelopment analysis Decision-making units Efficiency Optimization methods Simar and Wilson algorithm Statistical consistency R software Ciências Naturais::Matemáticas Parcerias para a implementação dos objetivos |
title_short |
Robust semi-parametric inference for two-stage production models: a beta regression approach |
title_full |
Robust semi-parametric inference for two-stage production models: a beta regression approach |
title_fullStr |
Robust semi-parametric inference for two-stage production models: a beta regression approach |
title_full_unstemmed |
Robust semi-parametric inference for two-stage production models: a beta regression approach |
title_sort |
Robust semi-parametric inference for two-stage production models: a beta regression approach |
author |
Ospina, Raydonal |
author_facet |
Ospina, Raydonal Baltazar, Samuel G. F. Leiva, Víctor Figueroa-Zúñiga, Jorge Castro, Cecília |
author_role |
author |
author2 |
Baltazar, Samuel G. F. Leiva, Víctor Figueroa-Zúñiga, Jorge Castro, Cecília |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Ospina, Raydonal Baltazar, Samuel G. F. Leiva, Víctor Figueroa-Zúñiga, Jorge Castro, Cecília |
dc.subject.por.fl_str_mv |
Asymmetry Bootstrapping Data envelopment analysis Decision-making units Efficiency Optimization methods Simar and Wilson algorithm Statistical consistency R software Ciências Naturais::Matemáticas Parcerias para a implementação dos objetivos |
topic |
Asymmetry Bootstrapping Data envelopment analysis Decision-making units Efficiency Optimization methods Simar and Wilson algorithm Statistical consistency R software Ciências Naturais::Matemáticas Parcerias para a implementação dos objetivos |
description |
The data envelopment analysis is related to a non-parametric mathematical tool used to assess the relative efficiency of productive units. In different studies on productive efficiency, it is common to employ semi-parametric procedures in two stages to determine whether any exogenous factors of interest affect the performance of productive units. However, some of these procedures, particularly those based on conventional statistical inference, generate inconsistent estimates when dealing with incoherent data-generating processes. This inconsistency arises due to the efficiency scores being limited to the unit interval, and the estimated scores often exhibit serial correlation and have limited observations. To address such inconsistency, several strategies have been suggested, with the most well-known being an algorithm based on a parametric bootstrap procedure using the truncated normal distribution and its regression model. In this work, we present a modification of this algorithm that utilizes the beta distribution and its regression structure. The beta model allows for better accommodation of asymmetry in the data distribution. Our proposed algorithm introduces inferential characteristics that are superior to the original algorithm, resulting in a more statistically coherent data-generating process and improving the consistency property. We have conducted computational experiments that demonstrate the improved results achieved by our proposal. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07 2023-07-01T00:00:00Z |
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 |
https://hdl.handle.net/1822/85512 |
url |
https://hdl.handle.net/1822/85512 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ospina, R.; Baltazar, S.G.F.; Leiva, V.; Figueroa-Zúñiga, J.; Castro, C. Robust Semi-Parametric Inference for Two-Stage Production Models: A Beta Regression Approach. Symmetry 2023, 15, 1362. https://doi.org/10.3390/sym15071362 2073-8994 10.3390/sym15071362 https://www.mdpi.com/2073-8994/15/7/1362 |
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 |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132465239949312 |