(untitled) (American Journal of Applied Statistics and Economics, 2024)
Corn is pivotal in global agriculture, serving diverse purposes in the food, feed, and biofuel sectors. Despite its economic significance, corn price volatility, influenced by supply-demand dynamics, climate variations, and geopolitical tensions, poses challenges in decision-making processes. This necessitates accurate price forecasting of corn for producers and government alike to formulate effective policies that uphold stability and enhance efficiency within the corn market. Using long-term records of the monthly global price of corn spanning from January 2014 to December 2023, this study employs SARIMA modeling techniques to forecast the global price of corn. To find a solution, the auto.arima() function from the “forecast” package in R 4.3.2 for Windows was employed to identify both the structure of the series (stationary or not) and type (seasonal or not) and sets the model’s parameters, which takes into account the AIC, AICc or BIC values generated to determine the best fitting seasonal ARIMA model. Following the Box–Jenkins methodology, the best-fitting SARIMA (0,1,1) (0,0,1) [12] model was identified, supported by the lowest AIC value. The Ljung–Box Q–test further validated the model’s adequacy in capturing the data’s behavior, with a non-significant p-value of 0.7013. This analysis uncovered valuable insights into the fluctuations of corn prices, providing a comprehensive understanding of the interplay between economic factors and external influences. This study underscores the practical utility of SARIMA modeling for farmers and other relevant stakeholders in anticipating market fluctuations and devising adaptive strategies in response to evolving corn market dynamics.