Unobserved Heterogeneity in Regression Models: A Semiparametric Approach Based on Nonlinear Sieves

dc.creatorMedeiros, Marcelo C
dc.creatorBurity, Priscilla
dc.creatorAssunção, Juliano
dc.date2015-10-05
dc.date.accessioned2022-11-03T21:19:23Z
dc.date.available2022-11-03T21:19:23Z
dc.identifierhttps://bibliotecadigital.fgv.br/ojs/index.php/bre/article/view/24305
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5048045
dc.descriptionThis paper proposes a semiparametric approach to control for unobserved heterogeneity in linear regression models, based on an artificial neural network extremum estimator. We present a procedure to specify the model and use simulations to evaluate its finite sample properties in comparison to alternative methods. The simulations show that our approach is less sensitive to increases in the dimensionality and complexity of the problem. We also use the model to study convergence of per capita income across Brazilian municipalities.en-US
dc.formatapplication/pdf
dc.languageeng
dc.publisherSociedade Brasileira de Econometriaen-US
dc.relationhttps://bibliotecadigital.fgv.br/ojs/index.php/bre/article/view/24305/44460
dc.rightsCopyright (c) 2015 Brazilian Review of Econometricspt-BR
dc.sourceBrazilian Review of Econometrics; Vol. 35 No. 1 (2015); 47-63en-US
dc.sourceBrazilian Review of Econometrics; v. 35 n. 1 (2015); 47-63pt-BR
dc.source1980-2447
dc.subjectSemiparametric modelsen-US
dc.subjectsieve extremum estimatorsen-US
dc.subjectneural networksen-US
dc.subjectconvergenceen-US
dc.subjectunobserved components.en-US
dc.subjectC14en-US
dc.titleUnobserved Heterogeneity in Regression Models: A Semiparametric Approach Based on Nonlinear Sievesen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


Este ítem pertenece a la siguiente institución