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- W2061205703 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.© 1992 John Wiley & Sons. Inc." @default.
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- W2061205703 date "1992-07-01" @default.
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- W2061205703 title "A comparison of selected methods for forecasting monthly alfalfa hay prices" @default.
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- W2061205703 doi "https://doi.org/10.1002/1520-6297(199207)8:4<309::aid-agr2720080404>3.0.co;2-m" @default.
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