A Comparison of Selected Methods for Forecasting Monthly Alfalfa Price

Document Type

Article

Journal/Book Title/Conference

Agribusiness, An International Journal

Volume

8

Issue

4

Publication Date

1992

First Page

309

Last Page

321

Abstract

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.

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