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Electricity price forecasting by averaging dynamic factor models

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Técnicas no Paramétricas y de Computación Intensiva en Estadísticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Modelización Estadística y Análisis de Datoses
dc.affiliation.institutoUC3M. Instituto Flores de Lemuses
dc.contributor.authorAlonso Fernández, Andrés Modesto
dc.contributor.authorBastos, Guadalupe
dc.contributor.authorGarcía-Martos, Carolina
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2023-11-03T12:36:28Z
dc.date.available2023-11-03T12:36:28Z
dc.date.issued2016-08-01
dc.description.abstractIn the context of the liberalization of electricity markets, forecasting prices is essential. With this aim, research has evolved to model the particularities of electricity prices. In particular, dynamic factor models have been quite successful in the task, both in the short and long run. However, specifying a single model for the unobserved factors is difficult, and it cannot be guaranteed that such a model exists. In this paper, model averaging is employed to overcome this difficulty, with the expectation that electricity prices would be better forecast by a combination of models for the factors than by a single model. Although our procedure is applicable in other markets, it is illustrated with an application to forecasting spot prices of the Iberian Market, MIBEL (The Iberian Electricity Market). Three combinations of forecasts are successful in providing improved results for alternative forecasting horizons.en
dc.description.sponsorshipAndrés M. Alonso acknowledges the support of the Ministry of Economy and Competitiveness, Spain, by Projects ECO2012-38442 and ECO2015-66593; Carolina García-Martos acknowledges financial support from Project DPI2011-23500, Ministry of Economy and Competitiveness, Spain; the authors would like to extend their appreciation to Professor Michael Wiper for his assistance and corrections regarding the proper use of English in this document.en
dc.format.extent21es
dc.identifier.bibliographicCitationAlonso, A., Bastos, G., & García‐Martos, C. (2016). Electricity price forecasting by averaging dynamic factor models. Energies, 9(8), 600.en
dc.identifier.doihttps://doi.org/10.3390/en9080600
dc.identifier.issn1996-1073
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue8, 600es
dc.identifier.publicationlastpage21es
dc.identifier.publicationtitleEnergies (Energies)en
dc.identifier.publicationvolume9es
dc.identifier.urihttps://hdl.handle.net/10016/38755
dc.identifier.uxxiAR/0000018274
dc.language.isoenges
dc.publisherMDPIen
dc.relation.projectIDGobierno de España. ECO2012-38442es
dc.relation.projectIDGobierno de España. ECO2015-66593es
dc.relation.projectIDGobierno de España. DPI2011-23500es
dc.rights© 2016 by the authors; licensee MDPI, Basel, Switzerlanden
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaEstadísticaes
dc.subject.otherMarketen
dc.subject.otherCombinationen
dc.subject.otherDimensionen
dc.subject.otherSelectionen
dc.subject.otherDimensionality reductionen
dc.subject.otherElectricity pricesen
dc.subject.otherBayesian model averagingen
dc.subject.otherForecast combinationen
dc.titleElectricity price forecasting by averaging dynamic factor modelsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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