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| Hamm, Lonnie; Brorsen, B. Wade. |
| 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. |
| Tipo: Journal Article |
Palavras-chave: Forecasting; Hog prices; Neural networks; ARIMA; Econometric; Agribusiness; Livestock Production/Industries. |
| Ano: 1997 |
URL: http://purl.umn.edu/90646 |
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| Manfredo, Mark R.; Sanders, Dwight R.. |
| USDA and Cooperative Extension Service forecasts of hog prices are directly tested for incremental value vis-à-vis futures-based forecasts in a forecast encompassing framework. At horizons less than six months, the lean hog futures-based forecast is found to be more accurate than both the USDA and Extension Service forecasts, and the difference in forecasting performance is statistically significant. Not only are the agency forecasts less accurate, but neither the USDA nor the Extension Service forecasts add incremental information relative to the futures forecast. The results suggest that extension forecasters may want to refocus forecasting efforts on basis relationships, longer forecast horizons, or commodities without active futures markets. |
| Tipo: Journal Article |
Palavras-chave: Forecast encompassing; Hog prices; Public forecasts; Demand and Price Analysis; Research Methods/ Statistical Methods. |
| Ano: 2004 |
URL: http://purl.umn.edu/59395 |
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