Transmuting unequally spaced data: a MIDAS regression touch to forecast real GDP growth in Brazil

Detalhes bibliográficos
Autor(a) principal: Ferreira, Julia Ladeira
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: https://hdl.handle.net/10438/29972
Resumo: Unequally spaced data poses a dilemma on how to aggregate high-frequency variables to model a low-frequency variable. To tackle this quandary, this work proposes to apply MI(xed) DA(ta) S(ampling) (MIDAS), which allows the independent and dependent variables to be sampled at various and different frequencies, to forecast the real GDP growth in Brazil using macroeconomic data. The results show that the restricted polynomial MIDAS specification can outperform the AR(1) for out of the sample recursively estimated nowcasts. Moreover, IBC-BR restricted lag polynomial based MIDAS showcase the best performance under all the computed metrics for evaluation. Not only did the restricted IBC-Br MIDAS outperform the benchmark, but it also beat the U-MIDAS. Fortuitously, the cumulative MSE ratio revealed that between 2014Q3 until the end of 2015, the quotient for the monetary base MIDAS model continuously declined. While this behavior might not be related to the "fiscal pedaling", its trend contributes to the economic policy narrative during those years.
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spelling Ferreira, Julia LadeiraEscolas::EESPMarçal, Emerson FernandesPrince, Diogo dePereira, Pedro L. Valls2021-01-05T15:01:47Z2021-01-05T15:01:47Z2020-12-16https://hdl.handle.net/10438/29972Unequally spaced data poses a dilemma on how to aggregate high-frequency variables to model a low-frequency variable. To tackle this quandary, this work proposes to apply MI(xed) DA(ta) S(ampling) (MIDAS), which allows the independent and dependent variables to be sampled at various and different frequencies, to forecast the real GDP growth in Brazil using macroeconomic data. The results show that the restricted polynomial MIDAS specification can outperform the AR(1) for out of the sample recursively estimated nowcasts. Moreover, IBC-BR restricted lag polynomial based MIDAS showcase the best performance under all the computed metrics for evaluation. Not only did the restricted IBC-Br MIDAS outperform the benchmark, but it also beat the U-MIDAS. Fortuitously, the cumulative MSE ratio revealed that between 2014Q3 until the end of 2015, the quotient for the monetary base MIDAS model continuously declined. While this behavior might not be related to the "fiscal pedaling", its trend contributes to the economic policy narrative during those years.Dados espaçados desigualmente impõem um dilema sobre como agregar variáveis de alta frequência. Este trabalho propõe a aplicação de MI(xed) DA(ta) S(ampling) (MIDAS), que permite modelar variáveis independentes e dependentes com diferentes frequências. Esse trabalho utiliza essa abordagem para prever o crescimento real do PIB no Brasil com séries macroeconômicos. Os resultados mostram que é possível superar a acurácia das previsões fora da amostra do AR(1) com a especificação polinomial recursivamente estimada. Dentre todos os regressores, o IBC-Br apresentou a melhor performance. O modelo com IBC-Br não apenas ultrapassou o desempenho do benchmark, mas também apresentou uma performance melhor do que o U-MIDAS. Por fim, o índice MSE acumulado revelou que, entre 2014Q3 e o final de 2015, o quociente para o modelo MIDAS da base monetária declinou continuamente. Embora esse comportamento possa não estar relacionado à "pedalada fiscal", sua tendência contribui para a narrativa da política econômica durante esses anos.engEconomic forecastingEconometric modelsGDPMacroeconomicsProduto interno bruto - BrasilPrevisão econômicaModelos econométricosMacroeconomiaMIDASEconomiaProduto interno bruto - BrasilPrevisão econômicaModelos econométricosMacroeconomiaTransmuting unequally spaced data: a MIDAS regression touch to forecast real GDP growth in Brazilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVLICENSElicense.txtlicense.txttext/plain; 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dc.title.eng.fl_str_mv Transmuting unequally spaced data: a MIDAS regression touch to forecast real GDP growth in Brazil
title Transmuting unequally spaced data: a MIDAS regression touch to forecast real GDP growth in Brazil
spellingShingle Transmuting unequally spaced data: a MIDAS regression touch to forecast real GDP growth in Brazil
Ferreira, Julia Ladeira
Economic forecasting
Econometric models
GDP
Macroeconomics
Produto interno bruto - Brasil
Previsão econômica
Modelos econométricos
Macroeconomia
MIDAS
Economia
Produto interno bruto - Brasil
Previsão econômica
Modelos econométricos
Macroeconomia
title_short Transmuting unequally spaced data: a MIDAS regression touch to forecast real GDP growth in Brazil
title_full Transmuting unequally spaced data: a MIDAS regression touch to forecast real GDP growth in Brazil
title_fullStr Transmuting unequally spaced data: a MIDAS regression touch to forecast real GDP growth in Brazil
title_full_unstemmed Transmuting unequally spaced data: a MIDAS regression touch to forecast real GDP growth in Brazil
title_sort Transmuting unequally spaced data: a MIDAS regression touch to forecast real GDP growth in Brazil
author Ferreira, Julia Ladeira
author_facet Ferreira, Julia Ladeira
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EESP
dc.contributor.member.none.fl_str_mv Marçal, Emerson Fernandes
Prince, Diogo de
dc.contributor.author.fl_str_mv Ferreira, Julia Ladeira
dc.contributor.advisor1.fl_str_mv Pereira, Pedro L. Valls
contributor_str_mv Pereira, Pedro L. Valls
dc.subject.eng.fl_str_mv Economic forecasting
Econometric models
GDP
Macroeconomics
topic Economic forecasting
Econometric models
GDP
Macroeconomics
Produto interno bruto - Brasil
Previsão econômica
Modelos econométricos
Macroeconomia
MIDAS
Economia
Produto interno bruto - Brasil
Previsão econômica
Modelos econométricos
Macroeconomia
dc.subject.por.fl_str_mv Produto interno bruto - Brasil
Previsão econômica
Modelos econométricos
Macroeconomia
MIDAS
dc.subject.area.por.fl_str_mv Economia
dc.subject.bibliodata.por.fl_str_mv Produto interno bruto - Brasil
Previsão econômica
Modelos econométricos
Macroeconomia
description Unequally spaced data poses a dilemma on how to aggregate high-frequency variables to model a low-frequency variable. To tackle this quandary, this work proposes to apply MI(xed) DA(ta) S(ampling) (MIDAS), which allows the independent and dependent variables to be sampled at various and different frequencies, to forecast the real GDP growth in Brazil using macroeconomic data. The results show that the restricted polynomial MIDAS specification can outperform the AR(1) for out of the sample recursively estimated nowcasts. Moreover, IBC-BR restricted lag polynomial based MIDAS showcase the best performance under all the computed metrics for evaluation. Not only did the restricted IBC-Br MIDAS outperform the benchmark, but it also beat the U-MIDAS. Fortuitously, the cumulative MSE ratio revealed that between 2014Q3 until the end of 2015, the quotient for the monetary base MIDAS model continuously declined. While this behavior might not be related to the "fiscal pedaling", its trend contributes to the economic policy narrative during those years.
publishDate 2020
dc.date.issued.fl_str_mv 2020-12-16
dc.date.accessioned.fl_str_mv 2021-01-05T15:01:47Z
dc.date.available.fl_str_mv 2021-01-05T15:01:47Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
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dc.identifier.uri.fl_str_mv https://hdl.handle.net/10438/29972
url https://hdl.handle.net/10438/29972
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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