{"id":{"repo_id":"brazil-fgv","oai_identifier":"oai:repositorio.fgv.br:10438/22986"},"canonical_url":"https://search.dev.ndltd.org/etd/brazil-fgv/oai:repositorio.fgv.br:10438/22986","repository":{"repo_id":"brazil-fgv","name":"Brazil FGV","base_url":"https://repositorio.fgv.br/server/oai/request"},"display":{"title":"Nowcasting Brazilian GDP: a performance assessment of dynamic factor models","abstract":"This work compares dynamic factor model’s forecasts for Brazilian GDP. Our approach takes into account mixed frequencies and can handle missing data. We implement three models: the first is based on the Principal Components Analysis methodology; the second employs a two-step estimation method with quarterly inputs; the last is similar to the former but uses monthly series. A real-time out-of-sample exercise is proposed to assess the performance of these models. A dataset is created for each day within 27 quarters - from the fourth quarter of 2010 up to the second quarter of 2017. For recent periods, the nowcasts estimated by both two-step procedures perform better than the average predictions of Focus Survey, a bulletin organized by the Brazilian Central Bank. We also show evidence that the average of GDP forecasts from this survey may be biased","abstract_html":"This work compares dynamic factor model’s forecasts for Brazilian GDP. Our approach takes into account mixed frequencies and can handle missing data. We implement three models: the first is based on the Principal Components Analysis methodology; the second employs a two-step estimation method with quarterly inputs; the last is similar to the former but uses monthly series. A real-time out-of-sample exercise is proposed to assess the performance of these models. A dataset is created for each day within 27 quarters - from the fourth quarter of 2010 up to the second quarter of 2017. For recent periods, the nowcasts estimated by both two-step procedures perform better than the average predictions of Focus Survey, a bulletin organized by the Brazilian Central Bank. We also show evidence that the average of GDP forecasts from this survey may be biased","abstract_has_math":false,"creators":["Gomes, Guilherme Branco"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Issler, João Victor"],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-19","date_published":"2018-03-19","updated_at":"2026-07-27T18:59:45Z","subjects":["GDP","Dynamic factor model","Nowcasting"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10438/22986","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Issler, João Victor"]},{"key":"dc:creator","label":"Author","values":["Gomes, Guilherme Branco"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-05-08T17:43:40Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-05-08T17:43:40Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-03-19"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["GDP","Dynamic factor model","Nowcasting"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10438/22986"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This work compares dynamic factor model’s forecasts for Brazilian GDP. Our approach takes into account mixed frequencies and can handle missing data. We implement three models: the first is based on the Principal Components Analysis methodology; the second employs a two-step estimation method with quarterly inputs; the last is similar to the former but uses monthly series. A real-time out-of-sample exercise is proposed to assess the performance of these models. A dataset is created for each day within 27 quarters - from the fourth quarter of 2010 up to the second quarter of 2017. For recent periods, the nowcasts estimated by both two-step procedures perform better than the average predictions of Focus Survey, a bulletin organized by the Brazilian Central Bank. We also show evidence that the average of GDP forecasts from this survey may be biased","Esse trabalho compara previsões para o PIB brasileiro utilizando modelos de fatores dinâmicos. Nossa abordagem leva em consideração frequências mistas e lida com dados incompletos na base (missing data). Nós implementamos três modelos: o primeiro é baseado na metodologia de componentes principais; o segundo emprega uma estimação por dois estágio com variáveis trimestrais; o último é similar ao anterior mas utiliza series mensais. Um exercício em tempo real, fora da amostra, é proposto para comparar o desempenho desses modelos. Uma base de dados é criada para cada dia dentro de 27 trimestres - do quarto trimestre de 2010 até o segundo de 2017. Para períodos recentes, os nowcasts estimados para ambos os procedimentos de dois estágios se mostram melhores do que a média de previsão da pesquisa Focus, um boletim organizado pelo Banco Central do Brasil. Nós também mostramos evidências que a média das previsões do PIB dessa pesquisa pode ser viesada"]},{"key":"dc:title","label":"Title","values":["Nowcasting Brazilian GDP: a performance assessment of dynamic factor models"]}]}],"canonical_facts":{"dc:contributor.advisor":["Issler, João Victor"],"dc:creator":["Gomes, Guilherme Branco"],"dc:date.accessioned":["2018-05-08T17:43:40Z"],"dc:date.available":["2018-05-08T17:43:40Z"],"dc:date.issued":["2018-03-19"],"dc:description.abstract":["This work compares dynamic factor model’s forecasts for Brazilian GDP. Our approach takes into account mixed frequencies and can handle missing data. We implement three models: the first is based on the Principal Components Analysis methodology; the second employs a two-step estimation method with quarterly inputs; the last is similar to the former but uses monthly series. A real-time out-of-sample exercise is proposed to assess the performance of these models. A dataset is created for each day within 27 quarters - from the fourth quarter of 2010 up to the second quarter of 2017. For recent periods, the nowcasts estimated by both two-step procedures perform better than the average predictions of Focus Survey, a bulletin organized by the Brazilian Central Bank. We also show evidence that the average of GDP forecasts from this survey may be biased","Esse trabalho compara previsões para o PIB brasileiro utilizando modelos de fatores dinâmicos. Nossa abordagem leva em consideração frequências mistas e lida com dados incompletos na base (missing data). Nós implementamos três modelos: o primeiro é baseado na metodologia de componentes principais; o segundo emprega uma estimação por dois estágio com variáveis trimestrais; o último é similar ao anterior mas utiliza series mensais. Um exercício em tempo real, fora da amostra, é proposto para comparar o desempenho desses modelos. Uma base de dados é criada para cada dia dentro de 27 trimestres - do quarto trimestre de 2010 até o segundo de 2017. Para períodos recentes, os nowcasts estimados para ambos os procedimentos de dois estágios se mostram melhores do que a média de previsão da pesquisa Focus, um boletim organizado pelo Banco Central do Brasil. Nós também mostramos evidências que a média das previsões do PIB dessa pesquisa pode ser viesada"],"dc:identifier.uri":["https://hdl.handle.net/10438/22986"],"dc:language.iso":["eng"],"dc:subject":["GDP","Dynamic factor model","Nowcasting"],"dc:title":["Nowcasting Brazilian GDP: a performance assessment of dynamic factor models"],"dc:type":["Dissertation"]},"updated_at":"2026-07-27T18:59:45Z"}