dc.contributor.author | Poza Lara, Carlos | |
dc.contributor.author | Monge Moreno, Manuel | |
dc.date.accessioned | 2022-10-25T10:50:38Z | |
dc.date.available | 2022-10-25T10:50:38Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 1350-4851 | spa |
dc.identifier.uri | https://hdl.handle.net/10641/3130 | |
dc.description.abstract | The 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.iso | eng | spa |
dc.publisher | Applied Economics Letters | spa |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Leading economic indicators | spa |
dc.subject | Business cycle | spa |
dc.subject | Google trends | spa |
dc.subject | Fractional integration | spa |
dc.subject | FCVAR model | spa |
dc.subject | Machine learning | spa |
dc.title | Forecasting Spanish economic activity in times of COVID-19 by means of the RT-LEI and machine learning techniques. | spa |
dc.type | journal article | spa |
dc.type.hasVersion | SMUR | spa |
dc.rights.accessRights | open access | spa |
dc.description.extent | 334 KB | spa |
dc.identifier.doi | 10.1080/13504851.2021.1994122 | spa |
dc.relation.publisherversion | https://www.tandfonline.com/doi/abs/10.1080/13504851.2021.1994122?journalCode=rael20 | spa |