Revista de Economia e Sociologia Rural
Revista de Economia e Sociologia Rural
Original Article

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

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Abstract: Given the relevance of corn for food and fuel industries, analysts and scholars are constantly comparing the forecasting accuracy of econometric models. These exercises test not only for the use of new approaches and methods, but also for the addition of fundamental variables linked to the corn market. This paper compares the accuracy of different usual models in financial macro-econometric literature for the period between 1995 and 2017. The main contribution lies in the use of transition regime models, which accommodate structural breaks and perform better for corn price forecasting. The results point out that the best models as those which consider not only the corn market structure, or macroeconomic and financial fundamentals, but also the non-linear trend and transition regimes, such as threshold autoregressive models.


forecasting, corn prices, accuracy, econometric models


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.


previsão, preços do milho, acurácia, modelos econométricos


Ahumada, H., & Cornejo, M. (2016). Forecasting food prices: the case of corn, soybeans and wheat. International Journal of Forecasting, 32(3), 838-848.

Albuquerquemello, V. P., Medeiros, R. K., Besarria, C. N., & Maia, S. F. (2018). Forecasting crude oil price: does exist an optimal econometric model? Energy, 155, 578-591.

Alvim, A. M., & Waquil, P. D. (2005). Efeitos do acordo entre o Mercosul e a União Européia sobre os mercados de grãos. Revista de Economia e Sociologia Rural, 43(4), 703-723.

Baffes, J., & Haniotis, T. (2010). Placing the 2006/08 commodity price boom into perspective. Washington, DC: World Bank.

Bastianin, A., Galeotti, M., & Manera, M. (2014). Causality and predictability in distribution: the ethanol–food price relation revisited. Energy Economics, 42, 152-160.

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. Economics, 3(2), 40-59.

Bobenrieth, H. E. S., Bobenrieth, H. J. R., & Wright, B. D. (2004). A model of supply of storage. Economic Development and Cultural Change, 52(3), 605-616.

Box, G. E., & Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association, 65(332), 1509-1526.

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. Empirical Economics, 59(1), 371-394.

Chan, K. S., & Tong, H. (1986). On estimating thresholds in autoregressive models. Journal of Time Series Analysis, 7(3), 179-190.

Cuaresma, J.C., Hlouskova, J., & Obersteiner, M. (2018). Fundamentals, speculation or macroeconomic conditions? Modelling and forecasting Arabica coffee prices. European Review of Agriculture Economics, 45(4), 583-615.

Diebold, F. X., & Mariano, R. S. (2002). Comparing predictive accuracy. Journal of Business & Economic Statistics, 20(1), 134-144.

Enders, W. (2008).Applied econometric time series. Chichester: John Wiley & Sons.

Favro, J., Caldarelli, C. E., & Camara, M. R. G. D. (2015). Modelo de análise da oferta de exportação de milho brasileira: 2001 a 2012. Revista de Economia e Sociologia Rural, 53(3), 455-476.

Food and Agriculture Organization of the United Nations – FAO. (2018). The future of food and agriculture. Alternative pathways to 2050. Rome: FAO. Retrieved in 2019, April 15, from

Gallagher, P. (1986). US corn yield capacity and probability: estimation and forecasting with nonsymmetric disturbances.North Central Journal of Agricultural Economics, 8(1), 109-122.

Gilbert, C. L. (2010). How to understand high food prices. Journal of Agricultural Economics, 61(2), 398-425.

Gurge, A. C. (2011). Impactos da política americana de estímulo aos biocombustíveis sobre a produção agropecuária e o uso da terra. Revista de Economia e Sociologia Rural, 49(1), 181-213.

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. Agricultural Economics, 46(S1), 157-171.

Hotelling, H. (1931). The economics of exhaustible resources. Journal of Political Economy, 39(2), 137-175.

Jadhav, V., Chinnappa, R. B., & Gaddi, G. M. (2017). Application of ARIMA model for forecasting agricultural prices. Journal of Agricultural Science and Technology, 19(4), 981-992.

Jayne, T. S., & Rashid, S. (2010).The value of accurate crop production forecasts. Michigan: Michigan State University.

Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics & Control, 12(2-3), 231-254.

Krishnamurthy, A., & Vissing-Jorgensen, A. (2011).The effects of quantitative easing on interest rates: channels and implications for policy. Cambridge: National Bureau of Economic.

Lütkepohl, H. (2005).New introduction to multiple time series analysis. Berlin: Springer Science & Business Media.

Mallory, M. L., Irwin, S. H., & Hayes, D. J. (2012). How market efficiency and the theory of storage link corn and ethanol markets. Energy Economics, 34(6), 2157-2166.

Mariscal, R., & Powell, A. (2014). Commodity price booms and breaks: detection, magnitude and implications for developing countries. Washington, DC: Inter-American Development Bank.

McPhail, L. L., Du, X., & Muhammad, A. (2012). Disentangling corn price volatility: the role of global demand, speculation, and energy.Journal of Agricultural and Applied Economics, 44, 401-410.

Osathanunkul, R., Khiewngamdee, C., Yamaka, W., & Sriboonchitta, S. (2018). The role of oil price in the forecasts of agricultural commodity prices. InInternational Conference of the Thailand Econometrics Society. Berlin: Springer.

Reichsfeld, D. A., & Roache, S. K. (2011).Do commodity futures help forecast spot prices?Washington, DC: International Monetary Fund.

Roberts, M. J., & Schlenker, W. (2009). World supply and demand of food commodity calories. American Journal of Agricultural Economics, 91(5), 1235-1242.

Roberts, M. J., & Schlenker, W. (2013). Identifying supply and demand elasticities of agricultural commodities: implications for the US ethanol mandate. The American Economic Review, 103(6), 2265-2295.

Runge, C. F., & Senauer, B. (2007). How biofuels could starve the poor. Foreign Affairs, 86, 41.

Serra, T., & Gil, J. M. (2013). Price volatility in food markets: can stock building mitigate price fluctuations? European Review of Agriculture Economics, 40(3), 507-528.

Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1-48.

Trujillo-Barrera, A., Mallory, M., & Garcia, P. (2012). Volatility spillovers in US crude oil, ethanol, and corn futures markets. Journal of Agricultural and Resource Economics, 37(2), 247-262.

United States Department of Agriculture – USDA. (2015). Agricultural Baseline Database: Corn. Retrieved in 2019, March 15, from

United States Department of Agriculture – USDA. (2019). Feed Oulook. Retrieved in 2019, March 15, from

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817-838.

Winkelried, D. (2016). Piecewise linear trends and cycles in primary commodity prices. Journal of International Money and Finance, 64, 196-213.

Winkelried, D. (2018). Unit roots, flexible trends, and the Prebisch-Singer hypothesis. Journal of Development Economics, 132, 1-17.

Xu, X. (2018). Cointegration and price discovery in US corn cash and futures markets. Empirical Economics, 55(4), 1889-1923.

Zhang, D., Zang, G., Li, J., Ma, K., & Liu, H. (2018). Prediction of soybean price in China using QR-RBF neural network model. Computers and Electronics in Agriculture, 154, 10-17.

Zivot, E., & Andrews, D. W. K. (2002). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 20(1), 25-44.

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