The role of transition regime models for corn prices forecasting
O papel de modelos de regime de transição para previsão de preços do milho
Vinícius Phillipe de Albuquerquemello; Rennan Kertlly de Medeiros; Diego Pitta de Jesus; Felipe Araujo de Oliveira
Abstract
Keywords
Resumo
Resumo: Dada a relevância do milho para as indústrias de alimentos e combustíveis, analistas e acadêmicos estão constantemente comparando a precisão das previsões dos modelos econométricos. Esses exercícios testam não apenas o uso de novas abordagens e métodos, mas também a adição de variáveis fundamentais ligadas ao mercado de milho. Este artigo compara a precisão de diferentes modelos usuais na literatura macro-econométrica financeira para o período entre 1995 e 2017. A principal contribuição está no uso de modelos de transição de regime, que acomodam quebras estruturais e têm melhor desempenho na previsão do preço do milho. Os resultados apontam que os melhores modelos são aqueles que consideram não apenas a estrutura do mercado de milho, ou fundamentos macroeconômicos e financeiros, mas também a tendência não linear e as transições de regime, como os modelos autorregressivos threshold.
Palavras-chave
Referências
Ahumada, H., & Cornejo, M. (2016). Forecasting food prices: the case of corn, soybeans and wheat.
Albuquerquemello, V. P., Medeiros, R. K., Besarria, C. N., & Maia, S. F. (2018). Forecasting crude oil price: does exist an optimal econometric model?
Alvim, A. M., & Waquil, P. D. (2005). Efeitos do acordo entre o Mercosul e a União Européia sobre os mercados de grãos.
Baffes, J., & Haniotis, T. (2010).
Bastianin, A., Galeotti, M., & Manera, M. (2014). Causality and predictability in distribution: the ethanol–food price relation revisited.
Beckers, B., & Beidas-Strom, S. (2015). Forecasting the Nominal Brent Oil Price with VARs—one model fits all? Washington: International Monetary Fund.
Benavides, G. (2009). Price volatility forecasts for agricultural commodities: an application of volatility models, option implieds and composite approaches for futures prices of corn and wheat.
Bobenrieth, H. E. S., Bobenrieth, H. J. R., & Wright, B. D. (2004). A model of supply of storage.
Box, G. E., & Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models.
Campos, B. C. (2020). Are there asymmetric relations between real interest rates and agricultural commodity prices? Testing for threshold effects of US real interest rates and adjusted wheat, corn, and soybean prices.
Chan, K. S., & Tong, H. (1986). On estimating thresholds in autoregressive models.
Cuaresma, J.C., Hlouskova, J., & Obersteiner, M. (2018). Fundamentals, speculation or macroeconomic conditions? Modelling and forecasting Arabica coffee prices.
Diebold, F. X., & Mariano, R. S. (2002). Comparing predictive accuracy.
Enders, W. (2008).
Favro, J., Caldarelli, C. E., & Camara, M. R. G. D. (2015). Modelo de análise da oferta de exportação de milho brasileira: 2001 a 2012.
Food and Agriculture Organization of the United Nations – FAO. (2018).
Gallagher, P. (1986). US corn yield capacity and probability: estimation and forecasting with nonsymmetric disturbances.
Gilbert, C. L. (2010). How to understand high food prices.
Gurge, A. C. (2011). Impactos da política americana de estímulo aos biocombustíveis sobre a produção agropecuária e o uso da terra.
Hoffman, L. A., Etienne, X. L., Irwin, S. H., Colino, E. V., & Toasa, J. I. (2015). Forecast performance of WASDE price projections for US corn.
Hotelling, H. (1931). The economics of exhaustible resources.
Jadhav, V., Chinnappa, R. B., & Gaddi, G. M. (2017). Application of ARIMA model for forecasting agricultural prices.
Jayne, T. S., & Rashid, S. (2010).
Johansen, S. (1988). Statistical analysis of cointegration vectors.
Krishnamurthy, A., & Vissing-Jorgensen, A. (2011).
Lütkepohl, H. (2005).
Mallory, M. L., Irwin, S. H., & Hayes, D. J. (2012). How market efficiency and the theory of storage link corn and ethanol markets.
Mariscal, R., & Powell, A. (2014).
McPhail, L. L., Du, X., & Muhammad, A. (2012). Disentangling corn price volatility: the role of global demand, speculation, and energy.
Osathanunkul, R., Khiewngamdee, C., Yamaka, W., & Sriboonchitta, S. (2018). The role of oil price in the forecasts of agricultural commodity prices. In
Reichsfeld, D. A., & Roache, S. K. (2011).
Roberts, M. J., & Schlenker, W. (2009). World supply and demand of food commodity calories.
Roberts, M. J., & Schlenker, W. (2013). Identifying supply and demand elasticities of agricultural commodities: implications for the US ethanol mandate.
Runge, C. F., & Senauer, B. (2007). How biofuels could starve the poor.
Serra, T., & Gil, J. M. (2013). Price volatility in food markets: can stock building mitigate price fluctuations?
Sims, C. A. (1980). Macroeconomics and reality.
Trujillo-Barrera, A., Mallory, M., & Garcia, P. (2012). Volatility spillovers in US crude oil, ethanol, and corn futures markets.
United States Department of Agriculture – USDA. (2015).
United States Department of Agriculture – USDA. (2019).
White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity.
Winkelried, D. (2016). Piecewise linear trends and cycles in primary commodity prices.
Winkelried, D. (2018). Unit roots, flexible trends, and the Prebisch-Singer hypothesis.
Xu, X. (2018). Cointegration and price discovery in US corn cash and futures markets.
Zhang, D., Zang, G., Li, J., Ma, K., & Liu, H. (2018). Prediction of soybean price in China using QR-RBF neural network model.
Zivot, E., & Andrews, D. W. K. (2002). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis.
Submetido em:
21/04/2020
Aceito em:
27/02/2021
Facebook
Google+
Twitter
LinkedIn
Mendeley
Email