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dc.contributor.authorSoberón Velez, Alexandra Pilar es_ES
dc.contributor.authorMusolesi, Antonioes_ES
dc.contributor.authorRodríguez-Poo, Juan M. es_ES
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2025-01-15T09:05:56Z
dc.date.issued2024es_ES
dc.identifier.issn0305-9049es_ES
dc.identifier.issn1468-0084es_ES
dc.identifier.otherPID2019-105986GB-C2es_ES
dc.identifier.otherTED2021-131763A-I00es_ES
dc.identifier.urihttps://hdl.handle.net/10902/34993
dc.description.abstractIn the analysis of the Griliches' knowledge capital production function, previous works pointed out the relevance of incorporating slope heterogeneity in the technological parameters, cross-sectional dependence arising simultaneously from common factors and spillovers, and possible nonlinear effects of relevant common observed variables. In order to solve the above problems, in this article we introduce a semi-parametric model in a partially linear form that copes simultaneously with all the previous specification issues. The asymptotic properties of the resulting estimators are obtained and the theoretical findings are further supported for small samples via several Monte Carlo experiments and an empirical applicationes_ES
dc.format.extent23 p.es_ES
dc.language.isoenges_ES
dc.publisherWiley-Blackwell Publishing Ltd.es_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceOxford Bulletin of Economics & Statistics, 2024, 86(4), 905-927es_ES
dc.titleA semi-parametric panel data model with common factors and spatial dependencees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1111/obes.12609es_ES
dc.rights.accessRightsembargoedAccesses_ES
dc.identifier.DOI10.1111/obes.12609es_ES
dc.type.versionacceptedVersiones_ES
dc.embargo.lift2027-01-01
dc.date.embargoEndDate2027-01-01es_ES


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Attribution-NonCommercial-NoDerivatives 4.0 InternationalExcepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International