Forecasting Births Using Google

[EN] Monitoring fertility change is particularly important for policy and planning purposes. New data may help us in this monitoring. We propose a new leading indicator based on Google web-searches. We then test its predictive power using US data. In a deep out-of sample comparison we show that popu...

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Detalles Bibliográficos
Autores: Billari, Francesco, D'Amuri, Francesco, Marcucci, Juri
Tipo de recurso: capítulo de libro
Fecha de publicación:2016
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/95605
Acceso en línea:https://riunet.upv.es/handle/10251/95605
Access Level:acceso abierto
Palabra clave:web data
internet data
big data
qca
pls
sem
conference
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repository_id_str
spelling Forecasting Births Using GoogleBillari, FrancescoD'Amuri, FrancescoMarcucci, Juriweb datainternet databig dataqcaplssemconference[EN] Monitoring fertility change is particularly important for policy and planning purposes. New data may help us in this monitoring. We propose a new leading indicator based on Google web-searches. We then test its predictive power using US data. In a deep out-of sample comparison we show that popular time series specifications augmented with web-search-related data improve their forecasting performance at forecast horizons of 6 to 24 months. The superior performance of these augmented models is confirmed by formal tests of equal forecast accuracy. Moreover, our results survive a falsification test and are confirmed also when a forecast horse race is conducted using different out-of-sample tests, and at the state rather than at the federal level. Conditioning on the same information set, the forecast error of our best model for predicting 2009 births is 35% lower than the Census bureau projections. Our findings indicate the potential use of Googe web-searches in monitoring fertility change and in informing fertility forecasts.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/10251/95605reponame: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:10251/956052026-06-13T07:49:27Z
dc.title.none.fl_str_mv Forecasting Births Using Google
title Forecasting Births Using Google
spellingShingle Forecasting Births Using Google
Billari, Francesco
web data
internet data
big data
qca
pls
sem
conference
title_short Forecasting Births Using Google
title_full Forecasting Births Using Google
title_fullStr Forecasting Births Using Google
title_full_unstemmed Forecasting Births Using Google
title_sort Forecasting Births Using Google
dc.creator.none.fl_str_mv Billari, Francesco
D'Amuri, Francesco
Marcucci, Juri
author Billari, Francesco
author_facet Billari, Francesco
D'Amuri, Francesco
Marcucci, Juri
author_role author
author2 D'Amuri, Francesco
Marcucci, Juri
author2_role author
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] Monitoring fertility change is particularly important for policy and planning purposes. New data may help us in this monitoring. We propose a new leading indicator based on Google web-searches. We then test its predictive power using US data. In a deep out-of sample comparison we show that popular time series specifications augmented with web-search-related data improve their forecasting performance at forecast horizons of 6 to 24 months. The superior performance of these augmented models is confirmed by formal tests of equal forecast accuracy. Moreover, our results survive a falsification test and are confirmed also when a forecast horse race is conducted using different out-of-sample tests, and at the state rather than at the federal level. Conditioning on the same information set, the forecast error of our best model for predicting 2009 births is 35% lower than the Census bureau projections. Our findings indicate the potential use of Googe web-searches in monitoring fertility change and in informing fertility forecasts.
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/10251/95605
url https://riunet.upv.es/handle/10251/95605
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
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score 15.301629

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