Sanders, Dwight R.

By: Kuethe, Todd H.; Hubbs, Todd; Sanders, Dwight R.
The USDA produces four forecasts of net farm income for each year; these forecasts are closely monitored by decision makers across the agricultural sector. However, little is known about the performance of these forecasts. Traditional forecast evaluation tests suggest that between 1975 and 2016, the long-horizon forecasts systematically under-predicted realized values. In addition, the shorter-horizon forecast revisions overreact to new information. The findings suggest that forecast users should adjust their expectations and that the USDA may want to consider other forecast approaches to supplement current procedures.
By: Sanders, Dwight R.; Irwin, Scott H.; Merrin, Robert P.
The forecasting content of the Commodity Futures Trading Commission’s Commitments of Traders (COT) report is investigated. Bivariate Granger causality tests show very little evidence that traders’ positions are useful in forecasting (leading) returns in 10 agricultural futures markets. However, there is substantial evidence that traders respond to price changes. In particular, noncommercial traders display a tendency for trend following. The other trader classifications display mixed styles, perhaps indicating those trader categories capture a variety of traders. The results generally do not support use of the COT data in predicting price movements in agricultural futures markets.
By: Sanders, Dwight R.; Garcia, Philip; Manfredo, Mark R.
The informational content in live cattle and hog deferred futures prices is assessed using a direct test of incremental forecast ability for two- to twelve-month horizons. For 1976-2007, the results indicate that hog futures prices add incremental information at all horizons, but unique information in live cattle prices declines quickly beyond the eight-month horizon with no incremental information at the twelve-month horizon. The contrast in performance is likely attributable to differences in the quality of public information and the nature of the production process.
By: Sanders, Dwight R.; Manfredo, Mark R.
An empirical methodology is developed for statistically testing the hedging effectiveness among competing futures contracts. The presented methodology is based on the encompassing principle, widely used in the forecasting literature, and applied here to minimum variance hedging regressions. Intuitively, the test is based on an alternative futures contract's ability to reduce residual basis risk by offering either diversification or a smaller absolute level of basis risk than a preferred futures contract. The methodology is easily extended to cases involving multiple hedging instruments and general hedge ratio models. Empirical applications suggest that the encompassing methodology can provide information beyond traditional approaches of comparing hedging effectiveness.
By: Sanders, Dwight R.; Manfredo, Mark R.
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.
By: Sanders, Dwight R.; Manfredo, Mark R.
One-step-ahead forecasts of quarterly beef, pork, and poultry production are examined and evaluated based on traditional criteria for optimality-efficiency and unbiasedness-as well as their performance versus a univariate time-series model. However, traditional regression methodology for evaluating forecasts is avoided due to interpretive issues. Instead, an empirical framework focusing on forecast errors in employed. Results suggest USDA forecasts are unbiased, but generally not efficient. That is, they do not fully incorporate the information contained in past forecasts. Moreover, USDA's predictions do not encompass all the information contained in forecasts generated by simple time-series models. Thus, practitioners who use the USDA forecasts may want to supplement them with time-series forecasts.