Econometric modelling of time series applied in the generation of subsidies in the milk production chain in Rio Grande do Sul
DOI:
https://doi.org/10.5902/2179460X41221Keywords:
Econometric Modelling, Milk Production, Cointegration vectorAbstract
The purpose of this article is to make short-term forecasting using the methodology Box & Jenkins, the Johansen method and the Granger causality, and the impulse-response function between variables price and milk production in Rio Grande do Sul’s market. The monthly price series of milk and its production in Rio Grande do Sul were analysed, in the period from January 1995 to December 2017. The model that suits best, for forecasts, the data of the series of the milk price paid to the producer was an ARIMA (1, 1, 1) and production was SARIMA (1, 1, 1) (1, 1, 1)6, which provided reasonable estimates of forecasts for the months from February to July of 2017. The use of Johansen methodologies identifies the existence of the one cointegration vector and a long-term equilibrium relation between variables price and production of the milk. When we analyse Granger's causality, the results point to a two-way relationship, that is, prices influence milk production and vice versa. The analysis of the impulse-response function showed that the shocks present significant impacts between production and cost, both in terms of duration and intensity.
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ANUALPEC. Anuário da Pecuária Brasileira. Pecuária de leite. São Paulo: IEG/FNP; 2017.
BLISKA FMM. Formação de preços de carne bovina: uma aplicação do modelo de auto-regressão vetorial. Agricultura em São Paulo. 1990;37(3):41-59.
BOX, G. E. P.; JENKINS, G. M. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day, 1970.
BOX, G. E. P.; JENKINS, G. M.; REINSEL, G. C. Time series analysis: forecasting and control. 3. ed. San Francisco: Holden-Day, 1994.
BOX GEP, JENKINS GM. Times series analysis: forecasting and control. Holden-day, San Francisco. 1976
BROOKS C. Introductory econometrics for finance. Cambrige: University Press; 2003.
CAMELO HN et al. Modelagem box – jenkins aplicada a previsão de velocidade do vento em regiões do nordeste brasileiro para fins de geração eólica. Ciência e Natura. 2018;40(30):1-10.
CEPEA – Centro de Estudo Avançados em Economia Aplicada. Boletim Informativo. São Paulo; 2017. Available from: https://www.cepea.esalq.usp.br/br
DALZOTTO-ARTUZO et al. Relação entre os custos de produção e o preço de mercado do milho. Custos e @gronegócio on line. 2017; 13(2):448.
EMBRAPA - Empresa Brasileira de Pesquisa Agropecuária. Gado de leite: importância econômica. São Paulo; 2017. Available from: https://sistemasdeproducao.cnptia.embrapa.br/FontesHTML/Leite/LeiteCerrado/importancia.html
EMBRAPA - Empresa Brasileira de Pesquisa Agropecuária. Anuário Leite 2018: indicadores, tendências e oportunidades para quem vive no setor leiteiro. São Paulo; 2018. Available from:
ENGLE RF. GRANGER CWJ. Co-integration and error correction: representation, estimation, and testing. Econometrica. 1987:55(2):251-276.
FAO - Food and Agriculture Organization of the United Nations. Dairy production and products: milk production. 2016. Available from: http://www.fao.org/agriculture/dairygateway
FAVA VL. Metodologia de Box-Jenkins para modelos univariados. In: Vasconcellos MAS, Alves D, editors. Manual de econometria: nível intermediário. São Paulo: Atlas; 2000.
FERRAZ, M SÁFADI T, LAGE G. Uso de modelos de séries temporais na previsão de séries de precipitação pluviais no município de Lavras – MG. Revista Brasileira de Agrometeorologia. 1999;7(2):259-267.
GRANGER CWJ. Investigating causal relations bu econometric models and cross – spectral models. Econometrica. 1969;37(3):424-438.
HALL AD, ANDERSON HM, GRANGER CWJ. A cointegration analysis of Treasyry Bill Yields. The Review of Economics and Statistics. 1992;74(1):116-126.
HARRIS RID. Cointegration analysis in econometric modelling. London: Prentice Hall, 1995.
IBGE - Instituto Brasileiro de Geografia e Estatística. Rio de Janeiro; 2017. Contas nacionais trimestrais. Available from: https://www.ibge.gov.br/
IBGE - Instituto Brasileiro de Geografia e Estatística. Rio de Janeiro; 2017. Pesquisa da pecuária municipal 2014. Available from: https://www.ibge.gov.br/
JOHANSEN S, JUSELIUS K. Maximum likelihood estimation and inference on cointegration: with applications to the demand for money. Oxford Bulletin of Economics and Statistics; 1990.
KOVAČEVIĆ V et al. Causality between corn production cost and cash corn price. Custos e @gronegócio on line. 2017;13(4):1-15.
LEAL JÚNIOR BV et al. Modelo híbrido de previsão de séries temporais para possíveis aplicações no setor de geração eólica. Ciência e Natura. 2018;40(Edição Especial: X Workshop Brasileiro de Micrometeorologia):1-5.
LÜTKEPOHL H. Introduction to multiple time series analysis. Berlin: Springer; 1991.
LÜTKEPOHL H, KRATZIG M (eds.). (2004) Applied Time Series Econometrics. Cambridge: Cambridge University Press; 2004.
MACHADO SLD, SILVA CR, ARAÚJO AA. Descrição temporal do comportamento do Cerrado sensu strictu usando séries temporais. Ciência e Natura. 2018;40(30):1-10.
MACKINNON JG. Model specification tests and artificial regressions. Journal of Economic Literature. 1992;30(1):102-146.
MACKINNON JG. Numerical Distribution Functions for Unit Root and Cointegration Tests. Journal of Applied Econometrics. 1996;11(6):601-618.
MACKINNON JG, HAUG AA, MICHELIS L. Numerical distribution functions of likelihood ratio tests for cointegration. Journal of Applied Econometrics. 1999;14(5):563–577.
MADDALA GS. Introduction to econometrics. New Jersey: Prentice-Hall; 1992.
MARCHEZAN A, SOUZA A. Previsão do preço dos principais grãos produzidos no Rio Grande do Sul. Ciência Rural. 2010; 40(11):2368-2374.
MARGARIDO MA Teste de co-integração de Johansen utilizando o SAS. Revista Agrícola. 2004;51(1):87-101.
MAPA - Ministry of Agriculture, Livestock and Supply. Brasília; 2017. Available from: http://www.agricultura.gov.br/
MORETTIN, P. A. e TOLOI, C. M. C., Modelos de Função de Transferência, Rio de Janeiro, 3ª Escola e séries Temporais Econometria, 1989.
NOMELINI QSS et al. Uso de modelagem univariada e multivariada com séries temporais como ferramenta de gestão do agronegócio na cultura de soja do Brasil. Revista Espacios. 2017;38(8).
OECD - Organisation for Economic Co-operation and Development. Projections agricoles 2018-2027. Paris; 2018. Available from: https://www.oecd-ilibrary.org/agriculture-and-food/data/statistiques-agricoles-de-l-ocde/perspectives-agricoles-de-l-ocde-et-de-la-fao-edition-2018_371b829bfr?parentId=http%3A%2F%2Finstance.metastore.ingenta.com%2Fcontent%2Fcollection%2Fagr-data-fr
OECD/FAO. Perspectives agricoles de l’OCDE e de la FAO. Estatísticas agrícolas da OCDE (base de dados), Paris-França. Paris; 2018. Available from: http://dx.doi.org/10.1787/agr-data-fr
PINHEIRO CAO, SENNA V. Previsão de preços através de redes neurais e análise espectral: evidências para o mercado futuro das commodities açúcar e soja. Custos e @gronegócio on line. 2017;13(4):1-26.
RETES-MANTILLA RF, TORRES-MANCERA MT, HERNÁNDEZ MLC Estimación del precio de compra de la pulpa de café en México para su aprovechamiento en la obtención de productos de alto valor agregado. Custos e @gronegócio on line. 2017; 13(3):101-119.
SANTANDER. A importância do agronegócio para o Brasil. 2017. Available from: https://www.santandernegocioseempresas.com.br
SIMS C. Macroeconomics and Reality. Econometrica. 1980;48(1):1-48.
SOUZA FM et al. (2010). Previsão do consumo de cimento no Estado do Rio Grande do Sul. Pesquisa Operacional para o Desenvolvimento. 2010; 2(1):1-86.
SOUZA FM. Modelos Box & Jenkins aplicados à previsão de demanda de leitos hospitalares [monography]. Santa Maria: Programa de Pós-Graduação em Estatística e Modelagem Quantitativos/UFSM; 2006.
SOUZA FM. Modelos de previsão: aplicações à energia elétrica – ARIMA – ARCH – AI e ACP. Curitiba: Appris; 2016.
SOUZA FM, SOUZA AM. Previsão para o fornecimento de energia elétrica para o RS por meio de combinação de previsão utilizando ponderação por autovalores. X Semana de Engenharia de Produção Sul Americana - SEPROSUL; 2010; Santiago; Chile; USACH – Universidade de Santiago do Chile, 1:1-10; 2010.
SOUZA, F. M.; LOPES, L. F. D. Previsão de demanda de leitos hospitalares por meio de análise de Séries Temporais. Revista Ciência e Natura. v. 31, n. 1. jun. 2009. p. 33-47
.
TERRA VIVA. Perspectivas agrícolas da OCDE e da FAO 2017-2026 – Produção. 2017. Available from: http://www.terraviva.com.br/site/index.php?option=com_k2&view=item&id=12467
TIBULO C, CARLI V. Previsão do preço do milho através de séries temporais. Scientia Plena. 2014;10(10).
TIBULO C, TIBULO V. Modelos de séries temporais para a previsão do preço médio mensal da soja no Rio Grande do Sul e análise da evolução da cultura no cenário nacional e regional. 2013. Available from: http://www.ucs.br
WANKE P, BARBOSA M. Combinação ou competição de previsões: um estudo de caso nos fretes do agronegócio. 2010. Available from: https://www.ilos.com.br/web/combinacao-ou-competicao-de-previsoes-um-estudo-de-caso-nos-fretes-do-agronegocio-parte-1/
WERNER L, RIBEIRO JLD. Previsão de demanda: uma aplicação dos modelos Box-Jenkins na área de assistência técnica de computadores pessoais. Gestão e Produção. 2003;10(1):47-67.
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