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dc.contributor.authorGianfreda, Angelica
dc.contributor.authorRavazzolo, Francesco
dc.contributor.authorRossini, Luca
dc.date.accessioned2021-09-17T16:03:51Z
dc.date.available2021-09-17T16:03:51Z
dc.date.created2020-04-28T17:01:58Z
dc.date.issued2020
dc.identifier.citationInternational Journal of Forecasting. 2020, 1-13.en_US
dc.identifier.issn0169-2070
dc.identifier.urihttps://hdl.handle.net/11250/2778963
dc.description.abstractWe compare alternative univariate versus multivariate models and frequentist versus Bayesian autoregressive and vector autoregressive specifications for hourly day-ahead electricity prices, both with and without renewable energy sources. The accuracy of point and density forecasts is inspected in four main European markets (Germany, Denmark, Italy, and Spain) characterized by different levels of renewable energy power generation. Our results show that the Bayesian vector autoregressive specifications with exogenous variables dominate other multivariate and univariate specifications in terms of both point forecasting and density forecasting.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleComparing the forecasting performances of linear models for electricity prices with high RES penetrationen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber974-986en_US
dc.source.volume36en_US
dc.source.journalInternational Journal of Forecastingen_US
dc.source.issue3en_US
dc.identifier.doi10.1016/j.ijforecast.2019.11.002
dc.identifier.cristin1808516
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal