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Forecasting Basis Levels in the Soybean Complex: A Comparison of Time Series Methods

Published online by Cambridge University Press:  28 April 2015

Dwight R. Sanders  and
Mark R. Manfredo
Dwight R. Sanders
Affiliation:
Department of Agribusiness Economics, Southern Illinois University, Carbondale, Illinois
Mark R. Manfredo
Affiliation:
Morrison School of Agribusiness and Resource Management, Arizona State University, Mesa, AZ
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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.

Keywords

basis forecaststime series modelssoybean complexC53Q13
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 38 , Issue 3 , December 2006 , pp. 513 - 523
Copyright
Copyright © Southern Agricultural Economics Association 2006

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