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Google Trends Topic-Based Uncertainty: A Multi-National Approach

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Google Trends Topic-Based Uncertainty: A Multi-National Approach

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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


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