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Nowcasting with Google Trends, the more is not always the better

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Nowcasting with Google Trends, the more is not always the better

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dc.contributor.author Combes, Stéphanie es_ES
dc.contributor.author Bortoli, Clément es_ES
dc.date.accessioned 2017-07-10T07:10:52Z
dc.date.available 2017-07-10T07:10:52Z
dc.date.issued 2016-10-10
dc.identifier.isbn 9788490484623
dc.identifier.uri http://hdl.handle.net/10251/84789
dc.description.abstract [EN] National accounts and macroeconomic indicators are usually published with a consequent delay. However, for decision makers, it is crucial to have the most up-to-date information about the current national economic situation. This motivates the recourse to statistical modeling to “predict the present”, which is referred to as “nowcasting”. Mostly, models incorporate variables from qualitative business tendency surveys available within a month, but forecasters have been looking for alternative sources of data over the last few years. Among them, searches carried out by users on research engines on the Internet – especially Google Trends – have been considered in several economic studies. Most of these exhibit an improvement of the forecasts when including one Google Trends series in an autoregressive model. But one may expect that the quantity and diversity of searches convey far more useful and hidden information. To test this hypothesis, we confronted different modeling techniques, traditionally used in the context of many variables compared to the number of observations, to forecast two French macroeconomic variables. Despite the automatic selection of many Google Trends, it appears that forecasts’ accuracy is not significantly improved with these approaches. es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.relation.ispartof CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics 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.title Nowcasting with Google Trends, the more is not always the better es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/CARMA2016.2015.4226
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Combes, S.; Bortoli, C. (2016). Nowcasting with Google Trends, the more is not always the better. En CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València. 15-22. doi:10.4995/CARMA2016.2015.4226. es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename CARMA 2016 - 1st International Conference on Advanced Research Methods and Analytics es_ES
dc.relation.conferencedate July 06-07,2016 es_ES
dc.relation.conferenceplace Valencia, Spain es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/CARMA/CARMA2016/paper/view/4226 es_ES
dc.description.upvformatpinicio 15 es_ES
dc.description.upvformatpfin 22 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela 4226 es_ES


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