US HFCS Price Forecasting Using Seasonal ARIMA Model

This paper focuses on forecasting US high fructose corn syrup (HFCS) prices using a seasonal autoregressive integrated moving average model. We use both monthly and quarterly data to forecast HFCS prices for the 1994–2015 period. We perform the Augmented Dickey–Fuller test for ensuring that the HFCS prices are stationary. We use mean absolute error, in–sample root mean square error, and out–of–sample root mean square error for evaluating the predictive accuracy of the models. Based on the out–of–sample performance, we found that the quarterly model performed well in predicting HFCS prices compared to monthly model. The results will help make better decision concerning the operation of corn-wet milling plant and HFCS production.


Subject(s):
Issue Date:
2016
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/230133
Total Pages:
21




 Record created 2017-04-01, last modified 2017-04-26

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