Investigating monthly beef cattle prices

Anderson established a three variable multiple regression model to explain the variation in the monthly seasonally adjusted price of Choice slaughter steers at Chicago from 1955 through 1960 (R²=.9955). This study was devoted to further investigation of beef cattle prices and factors influencing them. The same basic model was used with 1955 through 1961 data to explain Choice (R²=.9822) and Good (R²=.9919) steer prices at Chicago. However, before the relationship between price and the three predictor variables could be used for forecasting, each of the predictor variables had to be estimated. The three predictor variables investigated were fed cattle for slaughter, non-fed cattle for slaughter, and total personal income. Each was expressed on a per capita basis, thus requiring that population also be estimated.

Total cattle slaughter was estimated from the inventory for the previous year. Beef cattle marketings were estimated by two different methods. Annual marketings of non-fed cattle for slaughter were obtained as residuals by subtracting predicted fed marketings from predicted total cattle slaughter. A bi-monthly index was used to distribute the marketings throughout the year. Total personal income per capita was predicted on a monthly basis as a function of time. Monthly population increases were estimated from past average monthly increases. Predicted fed and non-fed cattle were divided by the estimated population and expressed on a per capita basis.

Monthly seasonally adjusted prices of Choice and Good steers were predicted over the period used to construct the model. The prediction equation was able to explain approximately 84 and 88 percent of the variation in the seasonally adjusted average monthly prices of Choice and Good steers, respectively.

The models developed in this investigation should prove useful for the entrepreneur. However, it is important that the user of the models recognize their limitations and adjust them according to his current economic environment.