Prevendo o Consumo Agregado das Famílias a Partir de Modelos VEC, VAR e Regressões MIDAS

Detalhes bibliográficos
Ano de defesa: 2023
Autor(a) principal: Arruda, Daniel Ferreira
Orientador(a): Ribeiro, Marcel Bertini
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Consumo agregado
Previsão
Correção de erros
Nowcasting
MIDAS
U-MIDAS
Palavras-chave em Inglês:
Aggregate consumption
Forecasting
Error correction
Link de acesso: https://hdl.handle.net/10438/33311
Resumo: This paper aims to evaluate the predictability of the Brazilian household aggregate consumption data in different time horizons. For the short and medium term, VEC and VAR models are estimated to capture the dynamics between aggregate household consumption and its main determinants. For the very short term (nowcasting), Mixed Data Sampling (MIDAS) regressions are estimated using amplified retail sales, consumer confidence and the IBC-Br, and varying the number of months known to the regressors within the forecasted quarter in each specification. Results indicate that the error correction models bring a slight improvement in the predictive performance in relation to the random walk, especially when including household credit growth or debt service. In addition, the MIDAS regressions show a good capability to predict consumption with regressors known within the quarter, again outperforming the random walk, and it is possible to state that the restricted MIDAS performs better than the unrestricted version (U-MIDAS).
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spelling Arruda, Daniel FerreiraEscolas::EESPMarçal, EmersonGomes, FábioRibeiro, Marcel Bertini2023-03-10T13:34:51Z2023-03-10T13:34:51Z2023-03-01https://hdl.handle.net/10438/33311This paper aims to evaluate the predictability of the Brazilian household aggregate consumption data in different time horizons. For the short and medium term, VEC and VAR models are estimated to capture the dynamics between aggregate household consumption and its main determinants. For the very short term (nowcasting), Mixed Data Sampling (MIDAS) regressions are estimated using amplified retail sales, consumer confidence and the IBC-Br, and varying the number of months known to the regressors within the forecasted quarter in each specification. Results indicate that the error correction models bring a slight improvement in the predictive performance in relation to the random walk, especially when including household credit growth or debt service. In addition, the MIDAS regressions show a good capability to predict consumption with regressors known within the quarter, again outperforming the random walk, and it is possible to state that the restricted MIDAS performs better than the unrestricted version (U-MIDAS).Esse trabalho tem por objetivo avaliar a previsibilidade da série brasileira de consumo agregado das famílias em diferentes horizontes de tempo. Para o curto e médio prazo, são estimados modelos VEC e VAR que capturam a dinâmica entre o consumo agregado das famílias e seus principais determinantes. Para o curtíssimo prazo (nowcasting), são estimadas regressões Mixed Data Sampling (MIDAS) empregando as vendas no varejo ampliado, a confiança do consumidor e o IBC-Br, e variando o número de meses conhecidos dos regressores dentro do trimestre previsto em cada especificação. Os resultados apontam que os modelos de correção de erro trazem leve melhora do desempenho preditivo em relação ao passeio aleatório, especialmente quando incorporam a variação do crédito às famílias ou o serviço da dívida. Além disso, as regressões MIDAS mostram boa capacidade de prever o consumo com regressores conhecidos dentro do trimestre, superando novamente o passeio aleatório, e é possível afirmar que o MIDAS restrito possui desempenho superior ao irrestrito (U-MIDAS).porAggregate consumptionForecastingError correctionConsumo agregadoPrevisãoCorreção de errosNowcastingMIDASU-MIDASEconomiaConsumo (Economia) - BrasilFamília - Aspectos econômicosModelos econométricosPrevendo o Consumo Agregado das Famílias a Partir de Modelos VEC, VAR e Regressões MIDASinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas 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dc.title.por.fl_str_mv Prevendo o Consumo Agregado das Famílias a Partir de Modelos VEC, VAR e Regressões MIDAS
title Prevendo o Consumo Agregado das Famílias a Partir de Modelos VEC, VAR e Regressões MIDAS
spellingShingle Prevendo o Consumo Agregado das Famílias a Partir de Modelos VEC, VAR e Regressões MIDAS
Arruda, Daniel Ferreira
Aggregate consumption
Forecasting
Error correction
Consumo agregado
Previsão
Correção de erros
Nowcasting
MIDAS
U-MIDAS
Economia
Consumo (Economia) - Brasil
Família - Aspectos econômicos
Modelos econométricos
title_short Prevendo o Consumo Agregado das Famílias a Partir de Modelos VEC, VAR e Regressões MIDAS
title_full Prevendo o Consumo Agregado das Famílias a Partir de Modelos VEC, VAR e Regressões MIDAS
title_fullStr Prevendo o Consumo Agregado das Famílias a Partir de Modelos VEC, VAR e Regressões MIDAS
title_full_unstemmed Prevendo o Consumo Agregado das Famílias a Partir de Modelos VEC, VAR e Regressões MIDAS
title_sort Prevendo o Consumo Agregado das Famílias a Partir de Modelos VEC, VAR e Regressões MIDAS
author Arruda, Daniel Ferreira
author_facet Arruda, Daniel Ferreira
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EESP
dc.contributor.member.none.fl_str_mv Marçal, Emerson
Gomes, Fábio
dc.contributor.author.fl_str_mv Arruda, Daniel Ferreira
dc.contributor.advisor1.fl_str_mv Ribeiro, Marcel Bertini
contributor_str_mv Ribeiro, Marcel Bertini
dc.subject.eng.fl_str_mv Aggregate consumption
Forecasting
Error correction
topic Aggregate consumption
Forecasting
Error correction
Consumo agregado
Previsão
Correção de erros
Nowcasting
MIDAS
U-MIDAS
Economia
Consumo (Economia) - Brasil
Família - Aspectos econômicos
Modelos econométricos
dc.subject.por.fl_str_mv Consumo agregado
Previsão
Correção de erros
Nowcasting
MIDAS
U-MIDAS
dc.subject.area.por.fl_str_mv Economia
dc.subject.bibliodata.por.fl_str_mv Consumo (Economia) - Brasil
Família - Aspectos econômicos
Modelos econométricos
description This paper aims to evaluate the predictability of the Brazilian household aggregate consumption data in different time horizons. For the short and medium term, VEC and VAR models are estimated to capture the dynamics between aggregate household consumption and its main determinants. For the very short term (nowcasting), Mixed Data Sampling (MIDAS) regressions are estimated using amplified retail sales, consumer confidence and the IBC-Br, and varying the number of months known to the regressors within the forecasted quarter in each specification. Results indicate that the error correction models bring a slight improvement in the predictive performance in relation to the random walk, especially when including household credit growth or debt service. In addition, the MIDAS regressions show a good capability to predict consumption with regressors known within the quarter, again outperforming the random walk, and it is possible to state that the restricted MIDAS performs better than the unrestricted version (U-MIDAS).
publishDate 2023
dc.date.accessioned.fl_str_mv 2023-03-10T13:34:51Z
dc.date.available.fl_str_mv 2023-03-10T13:34:51Z
dc.date.issued.fl_str_mv 2023-03-01
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 https://hdl.handle.net/10438/33311
url https://hdl.handle.net/10438/33311
dc.language.iso.fl_str_mv por
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dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional do FGV (FGV Repositório Digital)
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