EconPapers    
Economics at your fingertips  
 

Forecasting Hog Prices with a Neural Network

Lonnie Hamm and B Brorsen

Journal of Agribusiness, 1997, vol. 15, issue 01, 18

Abstract: Neural network models were compared to traditional forecasting methods in forecasting the quarterly and monthly farm price of hogs. A quarterly neural network model forecasted poorly in comparison to a quarterly econometric model. A monthly neural network model outperformed a monthly ARIMA model with respect to the mean square error criterion and performed similarly to the ARIMA model with respect to turning point accuracy. The more positive results of the monthly neural network model in comparison to the quarterly neural network model may be due to nonlinearities in the monthly data which are not in the quarterly data.

Keywords: Agribusiness; Livestock Production/Industries (search for similar items in EconPapers)
Date: 1997
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://ageconsearch.umn.edu/record/90646/files/JAB15one3.pdf (application/pdf)

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:ags:jloagb:90646

DOI: 10.22004/ag.econ.90646

Access Statistics for this article

More articles in Journal of Agribusiness from Agricultural Economics Association of Georgia Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().

 
Page updated 2025-03-28
Handle: RePEc:ags:jloagb:90646