Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018
Autor(a) principal: | |
---|---|
Data de Publicação: | 2016 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | http://hdl.handle.net/10438/15852 |
Resumo: | The aims of this work was forecast the demand for agricultural tractors in the Brazilian market during the triennium 2016-2018, using for this, techniques of time series econometrics, in this case, univariate models ARIMA and SARIMA and or multivariate models SARIMAX. Justified this research when holding the industry of agricultural machinery in Brazil, given the economic cycles and other external factors to the economic funda-mentals of demand, where it faces many challenges. Among this, demand estimation stands out because exert a strong impact, for example, planning and cost of short and medium term production, inventory levels, in relation to hand materials and suppliers of local labor and consequently in creating value to the shareholders. During literature review it was found several scientific papers address the agribusiness and its various areas, however, they were not found scientific papers published in Brazil that address the demand forecast of agricultural tractors in Brazil, which served as a motivation for add knowledge to the scientific world and value to the Brazilian market. It was concluded after testing with several models that are presented in the text and appendices, the model SARIMA (15, 1, 1) (1, 1, 1) fulfilled the assumptions set out in the specific objectives to choose the model that best fit itself to the data, and then was chosen as the model to forecast the demand for agricultural tractors in Brazil. These results point to a demand for agricultural tractors in Brazil oscillating between 46,000 and 49,000 units per year between the years 2016 and 2018. |
id |
FGV_8eab7e28da85146d6bb919d6a636eb2a |
---|---|
oai_identifier_str |
oai:repositorio.fgv.br:10438/15852 |
network_acronym_str |
FGV |
network_name_str |
Repositório Institucional do FGV (FGV Repositório Digital) |
repository_id_str |
3974 |
spelling |
Oliveira, Cristiano Dallagassa GontijoEscolas::EESPGurgel, Ângelo CostaVian, Carlos Eduardo de FreitasOzaki, Vitor AugustoOzaki, Vitor Augusto2016-03-14T16:14:31Z2016-03-14T16:14:31Z2016-02-16OLIVEIRA, Cristiano Dallagassa Gontijo. Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018. Dissertação (Mestrado Profissional em Agronegócios) - Escola de Economia de São Paulo, Fundação Getúlio Vargas - FGV, São Paulo, 2016.http://hdl.handle.net/10438/15852The aims of this work was forecast the demand for agricultural tractors in the Brazilian market during the triennium 2016-2018, using for this, techniques of time series econometrics, in this case, univariate models ARIMA and SARIMA and or multivariate models SARIMAX. Justified this research when holding the industry of agricultural machinery in Brazil, given the economic cycles and other external factors to the economic funda-mentals of demand, where it faces many challenges. Among this, demand estimation stands out because exert a strong impact, for example, planning and cost of short and medium term production, inventory levels, in relation to hand materials and suppliers of local labor and consequently in creating value to the shareholders. During literature review it was found several scientific papers address the agribusiness and its various areas, however, they were not found scientific papers published in Brazil that address the demand forecast of agricultural tractors in Brazil, which served as a motivation for add knowledge to the scientific world and value to the Brazilian market. It was concluded after testing with several models that are presented in the text and appendices, the model SARIMA (15, 1, 1) (1, 1, 1) fulfilled the assumptions set out in the specific objectives to choose the model that best fit itself to the data, and then was chosen as the model to forecast the demand for agricultural tractors in Brazil. These results point to a demand for agricultural tractors in Brazil oscillating between 46,000 and 49,000 units per year between the years 2016 and 2018.O objetivo desta dissertação foi estimar a demanda de tratores agrícolas para o mercado brasileiro no triênio 2016-2018, utilizando-se para isto de técnicas de econometria de séries temporais, neste caso, modelos univariados da classe ARIMA e SARIMA e ou multivariados SARIMAX. Justifica-se esta pesquisa quando se observa a indústria de máquinas agrícolas no Brasil, dados os ciclos econômicos e outros fatores exógenos aos fundamentos econômicos da demanda, onde esta enfrenta muitos desafios. Dentre estes, a estimação de demanda se destaca, pois exerce forte impacto, por exemplo, no planejamento e custo de produção de curto e médio prazo, níveis de inventários, na relação com fornecedores de materiais e de mão de obra local, e por consequência na geração de valor para o acionista. Durante a fase de revisão bibliográfica foram encontrados vários trabalhos científicos que abordam o agronegócio e suas diversas áreas de atuação, porém, não foram encontrados trabalhos científicos publicados no Brasil que abordassem a previsão da demanda de tratores agrícolas no Brasil, o que serviu de motivação para agregar conhecimento à academia e valor ao mercado através deste. Concluiu-se, após testes realizados com diversos modelos que estão dispostos no texto e apêndices, que o modelo univariado SARIMA (15,1,1) (1,1,1) cumpriu as premissas estabelecidas nos objetivos específicos para escolha do modelo que melhor se ajusta aos dados, e foi escolhido então, como o modelo para estimação da demanda de tratores agrícolas no Brasil. Os resultados desta pesquisa apontam para uma demanda de tratores agrícolas no Brasil oscilando entre 46.000 e 49.000 unidades ano entre os anos de 2016 e 2018.porEconomiaBox-Jenkins (1970)Modelos univariados e multivariadosEstimação de demandaDemanda de tratores agrícolasEconomiaTratores agrícolas - BrasilEconometriaAnálise de séries temporaisOferta e procuraIndústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018info: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/openAccessORIGINALDissertação Cristiano - FORMATADA E REVISADA 12.03 - PDF.pdfDissertação Cristiano - FORMATADA E REVISADA 12.03 - PDF.pdfDissertação de Mestradoapplication/pdf1509635https://repositorio.fgv.br/bitstreams/73d0a1c0-58d9-4d4e-bdeb-964b75c8f879/download504b76cb93f85ab2e09d5d2b8e04d4e1MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84707https://repositorio.fgv.br/bitstreams/719e3a5a-0df2-4a48-8448-d5965e6f8a4c/downloaddfb340242cced38a6cca06c627998fa1MD52TEXTDissertação Cristiano - FORMATADA E REVISADA 12.03 - PDF.pdf.txtDissertação Cristiano - FORMATADA E REVISADA 12.03 - PDF.pdf.txtExtracted texttext/plain104362https://repositorio.fgv.br/bitstreams/f68f748a-8629-4737-ac6a-cf73604e6afd/downloadc12ad4104fb83cea7ec5a3ccb22578f2MD55THUMBNAILDissertação Cristiano - FORMATADA E REVISADA 12.03 - PDF.pdf.jpgDissertação Cristiano - FORMATADA E REVISADA 12.03 - PDF.pdf.jpgGenerated Thumbnailimage/jpeg2654https://repositorio.fgv.br/bitstreams/7d1fa18c-75d7-47d3-a024-895e0604c550/download41d057dfbf2eef6fac4b89a589ec2ef3MD5610438/158522023-11-28 05:17:11.217open.accessoai:repositorio.fgv.br:10438/15852https://repositorio.fgv.brRepositório InstitucionalPRIhttp://bibliotecadigital.fgv.br/dspace-oai/requestopendoar:39742023-11-28T05:17:11Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas (FGV)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 |
dc.title.por.fl_str_mv |
Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018 |
title |
Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018 |
spellingShingle |
Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018 Oliveira, Cristiano Dallagassa Gontijo Economia Box-Jenkins (1970) Modelos univariados e multivariados Estimação de demanda Demanda de tratores agrícolas Economia Tratores agrícolas - Brasil Econometria Análise de séries temporais Oferta e procura |
title_short |
Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018 |
title_full |
Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018 |
title_fullStr |
Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018 |
title_full_unstemmed |
Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018 |
title_sort |
Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018 |
author |
Oliveira, Cristiano Dallagassa Gontijo |
author_facet |
Oliveira, Cristiano Dallagassa Gontijo |
author_role |
author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EESP |
dc.contributor.member.none.fl_str_mv |
Gurgel, Ângelo Costa Vian, Carlos Eduardo de Freitas Ozaki, Vitor Augusto |
dc.contributor.author.fl_str_mv |
Oliveira, Cristiano Dallagassa Gontijo |
dc.contributor.advisor1.fl_str_mv |
Ozaki, Vitor Augusto |
contributor_str_mv |
Ozaki, Vitor Augusto |
dc.subject.por.fl_str_mv |
Economia Box-Jenkins (1970) Modelos univariados e multivariados Estimação de demanda Demanda de tratores agrícolas |
topic |
Economia Box-Jenkins (1970) Modelos univariados e multivariados Estimação de demanda Demanda de tratores agrícolas Economia Tratores agrícolas - Brasil Econometria Análise de séries temporais Oferta e procura |
dc.subject.area.por.fl_str_mv |
Economia |
dc.subject.bibliodata.por.fl_str_mv |
Tratores agrícolas - Brasil Econometria Análise de séries temporais Oferta e procura |
description |
The aims of this work was forecast the demand for agricultural tractors in the Brazilian market during the triennium 2016-2018, using for this, techniques of time series econometrics, in this case, univariate models ARIMA and SARIMA and or multivariate models SARIMAX. Justified this research when holding the industry of agricultural machinery in Brazil, given the economic cycles and other external factors to the economic funda-mentals of demand, where it faces many challenges. Among this, demand estimation stands out because exert a strong impact, for example, planning and cost of short and medium term production, inventory levels, in relation to hand materials and suppliers of local labor and consequently in creating value to the shareholders. During literature review it was found several scientific papers address the agribusiness and its various areas, however, they were not found scientific papers published in Brazil that address the demand forecast of agricultural tractors in Brazil, which served as a motivation for add knowledge to the scientific world and value to the Brazilian market. It was concluded after testing with several models that are presented in the text and appendices, the model SARIMA (15, 1, 1) (1, 1, 1) fulfilled the assumptions set out in the specific objectives to choose the model that best fit itself to the data, and then was chosen as the model to forecast the demand for agricultural tractors in Brazil. These results point to a demand for agricultural tractors in Brazil oscillating between 46,000 and 49,000 units per year between the years 2016 and 2018. |
publishDate |
2016 |
dc.date.accessioned.fl_str_mv |
2016-03-14T16:14:31Z |
dc.date.available.fl_str_mv |
2016-03-14T16:14:31Z |
dc.date.issued.fl_str_mv |
2016-02-16 |
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.citation.fl_str_mv |
OLIVEIRA, Cristiano Dallagassa Gontijo. Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018. Dissertação (Mestrado Profissional em Agronegócios) - Escola de Economia de São Paulo, Fundação Getúlio Vargas - FGV, São Paulo, 2016. |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10438/15852 |
identifier_str_mv |
OLIVEIRA, Cristiano Dallagassa Gontijo. Indústria de máquinas agrícolas no Brasil: um modelo para estimação da demanda de tratores para o triênio 2016–2018. Dissertação (Mestrado Profissional em Agronegócios) - Escola de Economia de São Paulo, Fundação Getúlio Vargas - FGV, São Paulo, 2016. |
url |
http://hdl.handle.net/10438/15852 |
dc.language.iso.fl_str_mv |
por |
language |
por |
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) instname:Fundação Getulio Vargas (FGV) instacron:FGV |
instname_str |
Fundação Getulio Vargas (FGV) |
instacron_str |
FGV |
institution |
FGV |
reponame_str |
Repositório Institucional do FGV (FGV Repositório Digital) |
collection |
Repositório Institucional do FGV (FGV Repositório Digital) |
bitstream.url.fl_str_mv |
https://repositorio.fgv.br/bitstreams/73d0a1c0-58d9-4d4e-bdeb-964b75c8f879/download https://repositorio.fgv.br/bitstreams/719e3a5a-0df2-4a48-8448-d5965e6f8a4c/download https://repositorio.fgv.br/bitstreams/f68f748a-8629-4737-ac6a-cf73604e6afd/download https://repositorio.fgv.br/bitstreams/7d1fa18c-75d7-47d3-a024-895e0604c550/download |
bitstream.checksum.fl_str_mv |
504b76cb93f85ab2e09d5d2b8e04d4e1 dfb340242cced38a6cca06c627998fa1 c12ad4104fb83cea7ec5a3ccb22578f2 41d057dfbf2eef6fac4b89a589ec2ef3 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas (FGV) |
repository.mail.fl_str_mv |
|
_version_ |
1810023869945217024 |