The role of transition regime models for corn prices forecasting

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.

Comparison of linear and nonlinear models for forecasting of food commodity prices in Latvia

“…Having nonstationary, nonlinear data it is not recommended to take differences before fitting a threshold model [2], because classical unit root tests have low power in case of nonlinearity. Zivot-Andrews unit root test checks stationarity with breaks against a unit root in a time series.…”

“…V.P. de Albuquerquemello and others [2] show that the transition regime models for global corn prices give better forecast than the linear autoregressive models. M.A.Iquebal, Himardi Ghosh and Prajneshu [3] use three regime SETAR to Indian lac production data.…”

Yellow corn wholesale price forecasts via the neural network

“…They found that market fundamentals and macroeconomic developments contribute systematic predictive information for the forecast purpose. Albuquerquemello, Medeiros, Jesus and Oliveira (2021) assessed ARIMAs, VARs and their variations, particularly the consideration of transition regime models, for monthly U.S. corn price forecasts and pointed out the importance of incorporating nonlinear patterns in the model. Wan and Zhou (2021) examined corn futures price forecasts based on the ARIMA with data from China Dalian Commodity Exchange during 2018–2021 and concluded that a deeper consideration of parameter selection might improve model performance.…”