Modelación conjunta de media y varianza en modelos semiparamétricos autorregresivos espaciales

dc.contributorMelo Martínez, Oscar Orlando
dc.creatorToloza Delgado, Jurgen Daniel
dc.date.accessioned2021-01-19T23:24:13Z
dc.date.available2021-01-19T23:24:13Z
dc.date.created2021-01-19T23:24:13Z
dc.date.issued2020-01-18
dc.identifierToloza, J. (2020) Modelación conjunta de media y varianza en modelos semiparamétricos autorregresivos espaciales [Tesis de Maestría en Ciencias - Estadística, Universidad Nacional de Colombia] Repositorio Institucional
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/78835
dc.description.abstractIn this thesis, two spatial econometrics methodologies are proposed to estimate jointly the mean and the variance of the dependent variable, which have a spatial dependence whenare used in semiparametric autoregressive models. The proposed algorithms are based on the theory of generalized additive models for location, scale and shape; these methodologies allow to include nonparametric smoothing terms in the mean and variance of the considered models. The methods have a remarkable prediction capacity when they are compared in terms of the mean square error. In addition, they have a noteworthy estimation of the spatial autoregressive term, when they are compared with traditional ways of estimation (Anselin, 1988) and contemporary proposals of other authors (Basile & Mínguez, 2018). The proposed methodologies are applied in the construction of a hedonic price model for the cities of Bogotá and Boston. The results of the applications are notable due to their capacity of modelling the variability of housing prices in both locations.
dc.description.abstractDentro del contexto de la econometría espacial se proponen dos metodologías que permiten la modelación conjunta de media y varianza en modelos semiparamétricos autorregresivos con dependencia espacial en la variable dependiente. Los algoritmos desarrollados se fundamentan en los modelos aditivos generalizados para localización, escala y forma; los cuales permiten la inclusión de términos no paramétricos tanto en la media como la varianza. Se encuentra que los dos métodos propuestos tienen una destacable capacidad predictiva en términos del error cuadrático medio. Adicionalmente, evidencian una notable mejora en la estimación del parámetro espacial autorregresivo, respecto a otros métodos tradicionales (Anselin, 1988) y algunos desarrollos recientes (Basile & Mínguez, 2018). Las metodologías se emplean en la construcción de un modelo de precios hedónicos para las ciudades de Boston y Bogotá, destacando como principal resultado la capacidad de modelar la variabilidad del precio de las viviendas en estas localizaciones.
dc.languagespa
dc.publisherBogotá - Ciencias - Maestría en Ciencias - Estadística
dc.publisherDepartamento de Estadística
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
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dc.rightsAtribución-NoComercial 4.0 Internacional
dc.rightsAcceso abierto
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.titleModelación conjunta de media y varianza en modelos semiparamétricos autorregresivos espaciales
dc.typeOtro


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