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Please use this identifier to cite or link to this item: http://purl.umn.edu/43790

Title: Forecasting Basis Levels in the Soybean Complex: A Comparison of Time Series Methods
Authors: Sanders, Dwight R.
Manfredo, Mark R.
Keywords: basis forecasts
time series models
soybean complex
JEL Codes: C53
Q13
Issue Date: 2006-12
Abstract: A battery of time series methods are compared for forecasting basis levels in the soybean futures complex: soybeans, soybean meal, and soybean oil. Specifically, nearby basis forecasts are generated with exponential smoothing techniques, autoregression moving average (ARMA), and vector autoregression (VAR) models. The forecasts are compared to those of the 5-year average, year ago, and no change methods. Using the 5-year average as the benchmark method, the forecast evaluation results suggest that alternative naive techniques may produce better forecasts, and the improvement gained by time series modeling is relatively small. In this sample, there is little evidence that the basis has become systematically more difficult to forecast in recent years.
URI: http://purl.umn.edu/43790
Institution/Association: Journal of Agricultural and Applied Economics>Volume 38, Number 03, December 2006
Total Pages: 11
From Page: 513
To Page: 523
Collections:Volume 38, Number 03, December 2006

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