Standard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approach

Following Wooldridge (2014), we discuss and implement in Stata an efficient maximum likelihood approach to the estimation of corrected standard errors of two-stage optimization models. Specifically, we compare the robustness and efficiency of this estimate using different non-linear routines already...

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Autores:
Rios-Avila, Fernando
Canavire-Bacarreza, Gustavo
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad EAFIT
Repositorio:
Repositorio EAFIT
Idioma:
eng
OAI Identifier:
oai:repository.eafit.edu.co:10784/11432
Acceso en línea:
http://hdl.handle.net/10784/11432
Palabra clave:
Maximum Likelihood Estimation
non-linear models
endogeneity
two-step models
standard errors
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License
Acceso abierto
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spelling Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees2017-05-23T20:11:03Z2017-05-012017-05-23T20:11:03Zhttp://hdl.handle.net/10784/11432Following Wooldridge (2014), we discuss and implement in Stata an efficient maximum likelihood approach to the estimation of corrected standard errors of two-stage optimization models. Specifically, we compare the robustness and efficiency of this estimate using different non-linear routines already implemented in Stata such as ivprobit, ivtobit, ivpoisson, heckman, and ivregress.engUniversidad EAFITEscuela de Economía y FinanzasStandard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation ApproachworkingPaperinfo:eu-repo/semantics/workingPaperDocumento de trabajo de investigacióndrafthttp://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_8042Acceso abiertohttp://purl.org/coar/access_right/c_abf2Maximum Likelihood Estimationnon-linear modelsendogeneitytwo-step modelsstandard errorsgcanavir@eafit.edu.coRios-Avila, FernandoCanavire-Bacarreza, GustavoLICENSElicense.txtlicense.txttext/plain; charset=utf-82556https://repository.eafit.edu.co/bitstreams/1705c757-7710-41b6-8296-9ba09cf43528/download76025f86b095439b7ac65b367055d40cMD51ORIGINALWP-2017-09 Fernando Rios-Avila.pdfWP-2017-09 Fernando Rios-Avila.pdfDocumento de trabajo de investigaciónapplication/pdf607299https://repository.eafit.edu.co/bitstreams/d95deb28-d97d-46f4-a1aa-bc5bc6284563/downloade3f197e3d9e2bf0bef6ee3290dc1a247MD5210784/11432oai:repository.eafit.edu.co:10784/114322024-03-05 14:06:06.034open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co
dc.title.eng.fl_str_mv Standard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approach
title Standard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approach
spellingShingle Standard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approach
Maximum Likelihood Estimation
non-linear models
endogeneity
two-step models
standard errors
title_short Standard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approach
title_full Standard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approach
title_fullStr Standard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approach
title_full_unstemmed Standard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approach
title_sort Standard Error Correction in Two-Stage Optimization Models: A Quasi-Maximum Likelihood Estimation Approach
dc.creator.fl_str_mv Rios-Avila, Fernando
Canavire-Bacarreza, Gustavo
dc.contributor.eafitauthor.none.fl_str_mv gcanavir@eafit.edu.co
dc.contributor.author.none.fl_str_mv Rios-Avila, Fernando
Canavire-Bacarreza, Gustavo
dc.subject.keyword.spa.fl_str_mv Maximum Likelihood Estimation
non-linear models
endogeneity
two-step models
standard errors
topic Maximum Likelihood Estimation
non-linear models
endogeneity
two-step models
standard errors
description Following Wooldridge (2014), we discuss and implement in Stata an efficient maximum likelihood approach to the estimation of corrected standard errors of two-stage optimization models. Specifically, we compare the robustness and efficiency of this estimate using different non-linear routines already implemented in Stata such as ivprobit, ivtobit, ivpoisson, heckman, and ivregress.
publishDate 2017
dc.date.available.none.fl_str_mv 2017-05-23T20:11:03Z
dc.date.issued.none.fl_str_mv 2017-05-01
dc.date.accessioned.none.fl_str_mv 2017-05-23T20:11:03Z
dc.type.eng.fl_str_mv workingPaper
info:eu-repo/semantics/workingPaper
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_8042
dc.type.local.spa.fl_str_mv Documento de trabajo de investigación
dc.type.hasVersion.eng.fl_str_mv draft
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10784/11432
url http://hdl.handle.net/10784/11432
dc.language.iso.eng.fl_str_mv eng
language eng
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dc.rights.local.spa.fl_str_mv Acceso abierto
rights_invalid_str_mv Acceso abierto
http://purl.org/coar/access_right/c_abf2
dc.coverage.spatial.eng.fl_str_mv Medellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees
dc.publisher.spa.fl_str_mv Universidad EAFIT
dc.publisher.department.spa.fl_str_mv Escuela de Economía y Finanzas
institution Universidad EAFIT
bitstream.url.fl_str_mv https://repository.eafit.edu.co/bitstreams/1705c757-7710-41b6-8296-9ba09cf43528/download
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repository.name.fl_str_mv Repositorio Institucional Universidad EAFIT
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