Mostrar el registro sencillo del ítem
dc.contributor.author | Schütze, Florian![]() |
es_ES |
dc.date.accessioned | 2020-07-28T08:30:23Z | |
dc.date.available | 2020-07-28T08:30:23Z | |
dc.date.issued | 2020-05-12 | |
dc.identifier.isbn | 9788490488324 | |
dc.identifier.uri | http://hdl.handle.net/10251/148765 | |
dc.description.abstract | [EN] Several studies have shown that uncertainty among economic actorsinfluences business cycle dynamics. This paper uses Google Trends topic queries to construct an uncertainty proxy that can be applied to every country where Google is active. Using a VAR approach, this paper demonstrates that the obtained impulse-response functions of main economic indicators to a onestandard deviation shock to the constructed indicator, are similar to those from an already-existing uncertainty proxy, the EPU. This is true for the G7 countries and Russia. On average, the uncertainty indicator constructed for this paper leads to more statistically significant responses than does the EPU. Thus, this paper shows that Google Trends is a helpful tool for obtaining timely information about uncertainty among economic actors. The main improvement in this uncertainty proxy is in its language independence. Existing uncertaintymeasurement approaches, in contrast, rely on certain keywords that often vary across countries. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Editorial Universitat Politècnica de València | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Web data | es_ES |
dc.subject | Internet data | es_ES |
dc.subject | Big data | es_ES |
dc.subject | Qca | es_ES |
dc.subject | Pls | es_ES |
dc.subject | Sem | es_ES |
dc.subject | Conference | es_ES |
dc.subject | Google Trends | es_ES |
dc.subject | Uncertainty | es_ES |
dc.subject | Business Cycle Dynamics | es_ES |
dc.subject | VAR | es_ES |
dc.title | Google Trends Topic-Based Uncertainty: A Multi-National Approach | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.identifier.doi | 10.4995/CARMA2020.2020.11622 | |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | Schütze, F. (2020). Google Trends Topic-Based Uncertainty: A Multi-National Approach. Editorial Universitat Politècnica de València. 191-199. https://doi.org/10.4995/CARMA2020.2020.11622 | es_ES |
dc.description.accrualMethod | OCS | es_ES |
dc.relation.conferencename | CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics | es_ES |
dc.relation.conferencedate | Julio 08-09,2020 | es_ES |
dc.relation.conferenceplace | Valencia, Spain | es_ES |
dc.relation.publisherversion | http://ocs.editorial.upv.es/index.php/CARMA/CARMA2020/paper/view/11622 | es_ES |
dc.description.upvformatpinicio | 191 | |
dc.description.upvformatpfin | 199 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | OCS\11622 | es_ES |