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dc.contributor.authorPoza Lara, Carlos 
dc.contributor.authorMonge Moreno, Manuel 
dc.date.accessioned2022-10-25T10:50:38Z
dc.date.available2022-10-25T10:50:38Z
dc.date.issued2021
dc.identifier.issn1350-4851spa
dc.identifier.urihttps://hdl.handle.net/10641/3130
dc.description.abstractThe main aim of this paper is to analyse and estimate the behaviour of the Spanish economic activity in the next 12 months, by means of a Real-Time Leading Economic Indicator (RT-LEI), based on Google Trends, and the real GDP. We apply methodologies based on fractional integration and cointegration to measure the degree of persistence and to examine the long-term relationship. Finally, we carry out a forecast using a Machine Learning model based on an Artificial Neural Network. Our results indicate that the Spanish economy will experience a contraction in 1Q-21 and will require strong measures to reverse the situation and recover the original trend.spa
dc.language.isoengspa
dc.publisherApplied Economics Lettersspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectLeading economic indicatorsspa
dc.subjectBusiness cyclespa
dc.subjectGoogle trendsspa
dc.subjectFractional integrationspa
dc.subjectFCVAR modelspa
dc.subjectMachine learningspa
dc.titleForecasting Spanish economic activity in times of COVID-19 by means of the RT-LEI and machine learning techniques.spa
dc.typejournal articlespa
dc.type.hasVersionSMURspa
dc.rights.accessRightsopen accessspa
dc.description.extent334 KBspa
dc.identifier.doi10.1080/13504851.2021.1994122spa
dc.relation.publisherversionhttps://www.tandfonline.com/doi/abs/10.1080/13504851.2021.1994122?journalCode=rael20spa


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