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A comparison of selected methods for forecasting monthly alfalfa hay prices

โœ Scribed by R. K. Skaggs; Donald L. Snyder

Publisher
John Wiley and Sons
Year
1992
Tongue
English
Weight
664 KB
Volume
8
Category
Article
ISSN
0742-4477

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โœฆ Synopsis

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 pro- cedures 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.

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