Culminating Project Title
Date of Award
Culminating Project Type
Applied Economics: M.S.
School of Public Affairs
Ming Chien Lo
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Keywords and Subject Headings
Forecasting, Matlab, Statistics, Agriculture, Price, Corn
Of the current techniques used to forecast agricultural commodity prices, none carries as high of a cost as a supply and demand analysis. Because of this expense, firms that have the ability to produce forecasts that rely on supply and demand analysis, do not update their models very frequently. In this paper we will examine if statistical methodologies can provide price forecasts at least as accurate at supply and demand analysis techniques. Both statistical as well as supply and demand models will be evaluated at one, three, six, nine, and twelve month horizons. These horizons are typical for price forecasts that can be used for buying and selling activities, contract negotiations, and production decisions. Of the models we investigated an autoregressive model was found to provide the best price forecasts over a shorter horizon. Whereas a vector autoregressive model was shown to provide the best price forecasts beyond a six-month horizon. This study reveals that a few statistical techniques have the ability to outperformed models that incorporate supply and demand analysis in forecasting the price of U.S. corn.
Halonen, Daniel G., "Forecasting the Spot Price of Corn: Methods and Assessment" (2016). Culminating Projects in Economics. 5.