USDA Livestock Price Forecasts: A Comprehensive Evaluation

Article excerpt

One-step-ahead forecasts of quarterly live cattle, live hog, and broiler prices are evaluated under two general approaches: accuracy-based measures and classification-based measures which test the ability to categorize price movements directionally or within a forecasted range. Results suggest U.S. Department of Agriculture (USDA) price forecasts are not optimal. Broiler price forecasts are biased, and all the forecast series tend to repeat errors. While the USDA forecasts are more accurate than those of a univariate AR(4) time-series model, evidence suggests the USDA live cattle forecasts could be improved with a composite forecast that includes a time-series alternative. Despite this, the USDA correctly identifies the direction of price change in at least 70% of its forecasts over the sample period. Furthermore, actual prices fall within the USDA's forecasted range 48% of the time for broilers, but only 35% for hogs. Finally, there is some evidence that the USDA's price forecasting accuracy has improved over time for broilers, but has gotten marginally worse for hogs.

Key words: forecast efficiency, forecast evaluation, livestock prices, USDA forecasts


Tyson Foods, Inc., the world's largest producer, processor and marketer of chicken and poultry-based food products, today said that based on operating results through May, it continues to experience operating and margin pressures. These margin pressures are due to previous disruptions in the company's Russian market and to lower-than-expected prices received on its overall product mix....

- Tyson Foods, Inc., Springdale, Arkansas Press Release, june 12, 1997

For agricultural producers and agribusinesses, commodity prices directly affect costs, revenues, and profitability. For example, agribusiness giant Tyson Foods is involved in the production and processing of the three major meats: chicken, beef, and pork. In its public announcements, Tyson Foods clearly indicates that fluctuating meat prices directly affect the company's corporate earnings. Furthermore, Tyson's earnings projections necessarily rely on "forward-looking statements" about market prices (Tyson Foods, Inc.). To provide meaningful guidance to industry analysts, it is important that Tyson-and similar firms-understand and evaluate available price forecasts. Likewise, for smaller agribusinesses, price forecasts are crucial for planning business operations and making investment decisions.

Given the importance placed on agricultural prices, it is not surprising that commodity price forecasting, and the evaluation of forecasts, has long been an area of interest for economists [see, e.g., the early works of Green (1926) and Pettee (1936)]. In particular, forecasts provided by public agencies such as the U.S. Department of Agriculture's (USDA's) National Agricultural Statistics Service (NASS) are of interest. Producers, agribusinesses, and financial institutions use these forecasts to make production, marketing, and lending decisions (USDA/NASS). In fact, the objective of providing market outlook information is to enhance economic decision making, which leads to increased profits, utility, or social welfare (Freebairn). Accurate public forecasts for commodity prices can result in improved decision making by private forecasters, and also reduce market price variation (Smyth). Conversely, systematic errors in forecasts could lead to a misallocation of scarce resources (Stein). Thus, it is important that industry participants understand the uncertainty surrounding USDA price forecasts as well as any systematic biases or inefficiencies they may contain (Aaron).

Most research examining USDA forecasts focuses on the performance of USDA quantity or production forecasts in both crops (Irwin, Good, and Gomez) and livestock (Bailey and Brorsen). For example, as documented by Bailey and Brorsen, the USDA's annual beef and pork production forecasts, published in its "World Agricultural Supply and Demand Estimates" (WASDE) monthly report, are biased predictors over the entire 1982-1996 interval. …