Model-based single-month unemployment estimates from the Brazilian Labour Force Survey incorporating Google Trends data

Página do item simplificado
dc.creatorGonçalves, Caio César Soares
dc.creatorHidalgo, Luna
dc.creatorSilva, Denise Britz do Nascimento
dc.creatorBrakel, Jan Van den
dc.creatorGonçalves, Paulo Roberto Betbeder
dc.creator.Latteshttp://lattes.cnpq.br/6829577347369187
dc.creator.Latteshttp://lattes.cnpq.br/7341020472069469
dc.creator.Latteshttp://lattes.cnpq.br/0235269476708481
dc.creator.Lattes-
dc.creator.Latteshttp://lattes.cnpq.br/7495532284955897
dc.creator.affiliationFundação João Pinheiro
dc.creator.affiliationFundação Instituto Brasileiro de Geografia e Estatística, IBGE
dc.creator.affiliationEscola Nacional de Ciências Estatísticas
dc.creator.affiliationFundação João Pinheiro
dc.creator.orcidhttps://orcid.org/0000-0002-3366-7560
dc.creator.orcid-
dc.creator.orcidhttps://orcid.org/0000-0002-5514-7558
dc.creator.orcidhttps://orcid.org/0009-0009-8828-8574
dc.creator.orcid-
dc.date.accessioned2025-12-18T18:11:54Z
dc.date.available2025-12-18T18:11:54Z
dc.date.issued2025
dc.description.abstractenThis paper investigates the potential of incorporating Google Trends data into model-based unemployment estimates from the Brazilian Labour Force Survey (BLFS) to improve the precision and timeliness of official statistics. The study explores multivariate time series models that combine traditional survey data with big data sources, specifically Google search queries related to job seeking behaviour. The research addresses the growing demand for more frequent and precise labour market indicators, particularly at the state level and for specific demographic groups such as young people. The methodology employs state-space models and dynamic factor analysis to integrate unemployment statistics from the BLFS with Google Trends series. Variable selection techniques, including penalized regression elastic net and time series clustering with dynamic time warping distance, are used to identify relevant Google search terms. The analysis covers the period from January 2012 to December 2021, focusing on national estimates and two selected states: Minas Gerais (largest sample) and Roraima (smallest sample). Results demonstrate that incorporating Google Trends data can enhance the quality of unemployment estimates, particularly for areas with smaller sample sizes. The model-based approach demonstrates potential for producing single-month estimates and nowcast indicators, addressing the need for more timely labour market statistics. This research contributes to the literature on multi-source statistics and provides insights for national statistical offices seeking to leverage big data for improving official statistics production in developing countries.
dc.description.specialnotesTombo digital: FJP02-006168
dc.format.medium82 p. : il.
dc.identifier.citationGONÇALVES, C. C. S. et al. Model-based single-month unemployment estimates from the Brazilian Labour Force Survey incorporating Google Trends data. Belo Horizonte : FJP, 2025. (Texto para Discussão; 31)
dc.identifier.urihttps://repositorio.fjp.mg.gov.br/handle/123456789/4740
dc.language.isoen
dc.publisherFundação João Pinheiro
dc.relation.ispartofseriesTexto para Discussão; 31
dc.rightsopen access
dc.subject.thesaurusMercado de trabalho
dc.subject.thesaurusMão-de-obra
dc.subject.thesaurusBrasil
dc.titleModel-based single-month unemployment estimates from the Brazilian Labour Force Survey incorporating Google Trends data
dc.typeLivro
Arquivos
Pacote Original
Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
Model-based single-month_Texto_para_Discussão_FJP_31_2025.pdf
Tamanho:
5.98 MB
Formato:
Adobe Portable Document Format
Baixar
Licença do Pacote
Agora exibindo 1 - 1 de 1
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
1.48 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Baixar
Coleções
TEXTOS PARA DISCUSSÃO
FJP - GRUPO DE PESQUISA "ESTATÍSTICAS PÚBLICAS" (EstatPublicas)