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Modelling firm heterogeneity with spatial ‘trends’

dc.contributor.authorSarmiento, Camilo
dc.date.accessioned2024-02-01T15:09:14Z
dc.date.available2024-02-01T15:09:14Z
dc.date.issued2004
dc.identifier.issn1350–4851spa
dc.identifier.urihttps://repositorio.escuelaing.edu.co/handle/001/2805
dc.description.abstractThe hypothesis underlying this article is that firm heterogeneity can be captured by spatial characteristics of the firm (similar to the inclusion of a time trend in time series models). The hypothesis is examined in the context of modelling electric generation by coal powered plants in the presence of firm heterogeneity.eng
dc.description.abstractLa hipótesis que subyace a este artículo es que la heterogeneidad de las empresas puede captarse por características espaciales de la empresa (similar a la inclusión de una tendencia temporal en modelos de series de tiempo). La hipótesis se examina en el contexto del modelado eléctrico generación mediante centrales alimentadas por carbón en presencia de heterogeneidad empresarial.spa
dc.format.extent5 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherTaylor and Francis Groupspa
dc.sourcehttps://www.tandfonline.com/loi/rael20spa
dc.titleModelling firm heterogeneity with spatial ‘trends’eng
dc.typeArtículo de revistaspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_14cbspa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.contributor.researchgroupCentro de Estudios Económicosspa
dc.identifier.eissn1466–4291spa
dc.identifier.instnameUniversidad Escuela Colombiana de Ingeniería Julio Garavitospa
dc.identifier.reponameRepositorio Digitalspa
dc.identifier.repourlhttps://repositorio.escuelaing.edu.co/spa
dc.publisher.placeEstados Unidosspa
dc.relation.citationeditionNo. 11 Vol. 5 de 2004spa
dc.relation.citationendpage274spa
dc.relation.citationissue11spa
dc.relation.citationstartpage271spa
dc.relation.citationvolume5spa
dc.relation.ispartofjournalApplied Economics Letterseng
dc.relation.referencesBell, K. P. and Bockstael, N. E. (2000) Applying the generalized moments estimator approach to spatial problems involving micro-level data, Review of Economics and Statistics, 82, 72–82.spa
dc.relation.referencesCooley, T. F. and Prescott, E. C. (1973) Systematic (non-random) varying parameter regression: a theory and some applications, Annals of Economic and Social Measurement, 16, 463–74.spa
dc.relation.referencesDiewert, W. E. and Wales, T. J. (1992) Quadratic spline models for producer’s supply and demand functions, International Economic Review, 33, 705–22spa
dc.relation.referencesDubin, R. (1999) Spatial autocorrelation techniques for real estate data, Journal of Real Estate Literature, 7, 79–95.spa
dc.relation.referencesGriffiths, W. E., Hill, R. C. and Judge, G. G. (1993) Learning and Practicing Econometrics, Wiley, Chichester.spa
dc.relation.referencesJorgenson, D. W. and Griliches, Z. (1967) The explanation of productivity technological change, Review of Economics Studies, 34, 249–80.spa
dc.relation.referencesKelejian, H. and Robinson, D. (1995) Spatial correlation: a suggested alternative to the autocorrelation Model, in New Directions in Spatial Econometrics (Eds) L. Anselin and R. J. G. M. Florax, New York, Springer Verlag.spa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.subject.armarcEconomía matemática
dc.subject.armarcElasticidad (economía)
dc.subject.armarcProductividad industrial
dc.subject.armarcHeterogeneidad empresarial
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa


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