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

[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 pr...

ver descrição completa

Detalhes bibliográficos
Autores: Combes, Stéphanie, Bortoli, Clément
Tipo de documento: capítulo de livro
Data de publicação:2016
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositório:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglês
OAI Identifier:oai:riunet.upv.es:10251/84789
Acesso em linha:https://riunet.upv.es/handle/10251/84789
Access Level:Acceso aberto
Palavra-chave:web data
internet data
big data
qca
pls
sem
conference
id ES_1d392131262da4110feb06b0975bc2c7
oai_identifier_str oai:riunet.upv.es:10251/84789
network_acronym_str ES
network_name_str España
repository_id_str
spelling Nowcasting with Google Trends, the more is not always the betterCombes, StéphanieBortoli, Clémentweb datainternet databig dataqcaplssemconference[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.Editorial Universitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20162016-10-10book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/84789reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/847892026-06-13T07:49:27Z
dc.title.none.fl_str_mv Nowcasting with Google Trends, the more is not always the better
title Nowcasting with Google Trends, the more is not always the better
spellingShingle Nowcasting with Google Trends, the more is not always the better
Combes, Stéphanie
web data
internet data
big data
qca
pls
sem
conference
title_short Nowcasting with Google Trends, the more is not always the better
title_full Nowcasting with Google Trends, the more is not always the better
title_fullStr Nowcasting with Google Trends, the more is not always the better
title_full_unstemmed Nowcasting with Google Trends, the more is not always the better
title_sort Nowcasting with Google Trends, the more is not always the better
dc.creator.none.fl_str_mv Combes, Stéphanie
Bortoli, Clément
author Combes, Stéphanie
author_facet Combes, Stéphanie
Bortoli, Clément
author_role author
author2 Bortoli, Clément
author2_role author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv web data
internet data
big data
qca
pls
sem
conference
topic web data
internet data
big data
qca
pls
sem
conference
description [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.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016-10-10
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/84789
url https://riunet.upv.es/handle/10251/84789
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869404268010668033
score 15,198674

Registros relacionados