Nowcasting Brazilian GDP

Detalhes bibliográficos
Autor(a) principal: Mattos, Pedro Montero
Data de Publicação: 2017
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: http://hdl.handle.net/10438/18775
Resumo: Based on recent surveys on nowcasting methods, we apply the one-step estimation of dynamic factor models to the Brazilian case. Such methodology copes well with the problems of mixed-frequency series, ragged edges, timeliness and high dimensionality of data sets. We use the daily expectation published by the Brazilian Central Bank as a benchmark for our model and we do not find enough evidence to reject that both models have equal predictive accuracy, under non-distressed circumstances.
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spelling Mattos, Pedro MonteroEscolas::EESPMarçal, Emerson FernandesSoares, Gustavo BarbosaMasini, Ricardo Pereira2017-09-12T17:16:25Z2017-09-12T17:16:25Z2017-08-16http://hdl.handle.net/10438/18775Based on recent surveys on nowcasting methods, we apply the one-step estimation of dynamic factor models to the Brazilian case. Such methodology copes well with the problems of mixed-frequency series, ragged edges, timeliness and high dimensionality of data sets. We use the daily expectation published by the Brazilian Central Bank as a benchmark for our model and we do not find enough evidence to reject that both models have equal predictive accuracy, under non-distressed circumstances.Baseado em recentes pesquisas em métodos de Nowcasting, foi aplicada a estimação de modelos de fatores dinâmicos em um passo ao caso brasileiro. Esta metodologia lida com os problemas de frequências mistas, amostras recortadas, horizonte temporal e alta dimensão da amostra. Foram utilizadas as expectativas diárias do PIB publicadas pelo Banco Central como um benchmark do modelo. Não foram encontradas evidências que rejeitam a hipótese de igual poder preditivo, para circunstâncias econômicas não estressadas.engNowcastingForecastingDynamic factor modelsEconomiaPrevisão econômica - BrasilBrasil - Condições econômicasModelos não lineares (Estatística)Produto interno bruto - BrasilMacroeconomiaNowcasting Brazilian GDPinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessTEXTnowcasting-brazilian-gdp-final.pdf.txtnowcasting-brazilian-gdp-final.pdf.txtExtracted 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dc.title.eng.fl_str_mv Nowcasting Brazilian GDP
title Nowcasting Brazilian GDP
spellingShingle Nowcasting Brazilian GDP
Mattos, Pedro Montero
Nowcasting
Forecasting
Dynamic factor models
Economia
Previsão econômica - Brasil
Brasil - Condições econômicas
Modelos não lineares (Estatística)
Produto interno bruto - Brasil
Macroeconomia
title_short Nowcasting Brazilian GDP
title_full Nowcasting Brazilian GDP
title_fullStr Nowcasting Brazilian GDP
title_full_unstemmed Nowcasting Brazilian GDP
title_sort Nowcasting Brazilian GDP
author Mattos, Pedro Montero
author_facet Mattos, Pedro Montero
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EESP
dc.contributor.member.none.fl_str_mv Marçal, Emerson Fernandes
Soares, Gustavo Barbosa
dc.contributor.author.fl_str_mv Mattos, Pedro Montero
dc.contributor.advisor1.fl_str_mv Masini, Ricardo Pereira
contributor_str_mv Masini, Ricardo Pereira
dc.subject.eng.fl_str_mv Nowcasting
Forecasting
Dynamic factor models
topic Nowcasting
Forecasting
Dynamic factor models
Economia
Previsão econômica - Brasil
Brasil - Condições econômicas
Modelos não lineares (Estatística)
Produto interno bruto - Brasil
Macroeconomia
dc.subject.area.por.fl_str_mv Economia
dc.subject.bibliodata.por.fl_str_mv Previsão econômica - Brasil
Brasil - Condições econômicas
Modelos não lineares (Estatística)
Produto interno bruto - Brasil
Macroeconomia
description Based on recent surveys on nowcasting methods, we apply the one-step estimation of dynamic factor models to the Brazilian case. Such methodology copes well with the problems of mixed-frequency series, ragged edges, timeliness and high dimensionality of data sets. We use the daily expectation published by the Brazilian Central Bank as a benchmark for our model and we do not find enough evidence to reject that both models have equal predictive accuracy, under non-distressed circumstances.
publishDate 2017
dc.date.accessioned.fl_str_mv 2017-09-12T17:16:25Z
dc.date.available.fl_str_mv 2017-09-12T17:16:25Z
dc.date.issued.fl_str_mv 2017-08-16
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10438/18775
url http://hdl.handle.net/10438/18775
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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institution FGV
reponame_str Repositório Institucional do FGV (FGV Repositório Digital)
collection Repositório Institucional do FGV (FGV Repositório Digital)
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