Jon A. Brandt

Composite Forecasting: An Application with U.S. Hog Prices

Producers, processors, and distributors of agricultural commodities make decisions in a risky environment. Uncertain production and relatively low price elasticities of demand provide the setting for rather large fluctuations in commodity prices. Sensible decision making thus requires information about the likelihood of many alternative outcomes. Such information can be obtained from both private and public sources through price forecasts. Decision makers often have several forecasts on

Forecasting and Hedging: An Illustration of Risk Reduction in the Hog Industry

Hog producers and first handlers can reduce the risk of unfavorable price fluctuations by combining the information from forecasting models with a selective hedging strategy. Six quarterly price-forecasting approaches (econometric, ARIMA, expert, naive, and two composites) were evaluated over the 1976–82 period using a simple yet pragmatic hedging decision rule. The results indicate that relatively modest improvements in prices received by producers or paid by first-handlers relative to cash marketing were possible. Statistically significant reductions in short-term risk exposure were achieved by both groups through most of the approaches evaluated.

FORECASTING WITH VECTOR AUTOREGRESSIONS VERSUS A UNIVARIATE ARIMA PROCESS: AN EMPIRICAL EXAMPLE WITH U.S. HOG PRICES

Efforts to improve the accuracy of forecasts have turned toward more sophisticated prediction approaches. The use of vector autoregressions (VAR) as an alternative to more simple univariate time series processes reflects, in part, this move. The VAR approach incorporates the information from more than one economic time series into the forecasting process. Out-of-sample quarterly forecasts of hog prices were generated over the 1976-1982 period via a VAR and an univariate ARIMA process. The results indicated no improvement in forecasting ability by the more complex VAR procedure based on several measures of performance.

Mechanical Harvesting and the California Tomato Industry: A Simulation Analysis

Simulation analysis based on an econometric model was used to compare processing-tomato industry performance with and without the development of mechanical harvesting and under differing wage scales. Expanded production under mechanical harvesting increased requirements for preharvest, seasonal and off-season cannery, and assembly labor relative to continued hand harvest. This offset much, but not all, of the reduction in seasonal harvest labor. Employment shifted more toward jobs of higher skill and pay. Product prices were lower. Economic surplus measures suggest consumers have been the primary long-run benefactors of mechanical harvest adoption.