A comparison of selected methods for forecasting monthly alfalfa hay prices

R. K. Skaggs and Donald Snyder
Additional contact information
R. K. Skaggs: New Mexico State University Department of Agricultural Economics and Agricultural Business, Postal: New Mexico State University Department of Agricultural Economics and Agricultural Business

Agribusiness, 1992, vol. 8, issue 4, 309-321

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.© 1992 John Wiley & Sons. Inc.

Date: 1992
References: View references in EconPapers View complete reference list from CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:agribz:v:8:y:1992:i:4:p:309-321

DOI: 10.1002/1520-6297(199207)8:4<309::AID-AGR2720080404>3.0.CO;2-M

Access Statistics for this article

Agribusiness is currently edited by Ronald W. Cotterill

More articles in Agribusiness from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().