A Comparison of Selected Methods for Forecasting Monthly Alfalfa Price
Agribusiness, An International Journal
Alfalfa hay is one of the most important field crops in the United States; however, data limitations make it difficult to analyze and forecast hay prices. This research applied nine alternative procedures to generate postsample forecasts of California alfalfa hay prices. The procedures tested were: classical decomposition, exponential smoothing, univariate stochastic, multiple regression, bivariate stochastic, vector autoregression (unrestricted, Bayesian restricted and stepwise variable selection), and a structurally based system. Postsample predictive accuracy was evaluated; results indicated the simple procedures performed well in forecasting hay prices and limited improvement in accuracy with increased model complexity and information set.
Skaggs, R., and D.L. Snyder. A Comparison of Selected Methods for Forecasting Monthly Alfalfa Price. Agribusiness, An International Journal 8(4, July 1992):309-21.