Extracción de los ejemplos

“Weekly and Monthly Dodge City, KS Feeder Cattle Prices” file was used for cash feeder cattle prices. This was the most complete representative feeder cattle cash price data set we had available to us. While this western Kansas data set isn’t perfectly representative of Flint Hills regional feeder cattle prices, we believe it provides strong insights into historical prices within the region. It is likely that prices are slightly higher in western Kansas due to relatively close proximity to feedyards and slaughter facilities. The “Daily and Weekly Feeder Futures Prices” file from LMIC was used for feeder cattle futures price data.

In both data sets there were missing values. Multiple steps were taken to fill the missing values.

Three stages were used to fill in values for the missing observations for the western Kansas auction data. Each stage consisted of multiple steps. If there was not missing data in the table, the values remained the same. If a value was missing within the table, the first step in calculating the replacement was to average the cash value from the prior week and the cash value from the following week. A separate process was required for instances in which both the

previous and following weeks cash values weren’t available. An average value was calculated for the entire data set for each set of weight classes. These values were then used to yield carry values. Yield carry values are the difference between the average of one weight class and the next. The given values from the preceding weight classes were used and the carry values were subtracted to obtain approximated cash values. Cash prices were generated for missing values of cattle weighing from 200-1,100 pounds. Only 21.84% of observations were missing before filling the data set for the weight classes most applicable to our model (400-800 pound animals). After the first stage of filling blanks was completed only 0.89% of observations for the 400-800 pound weight classes were missing and 7.96% of the observations within the entire data set (200- 1,100 pound weight classes) were missing.

Although, this first fill adjusted nearly all blanks for the weight classes used in the model, two more steps were taken to fill the remaining blanks within the entire data set. This will allow the model to be expanded to lighter or heavier animals in the future, or for other future research projects. For stage two of imputing missing observations the process was repeated using the data generated in stage one. If a value was available this observation was used. If an observation was missing the value from the prior week was averaged with the value from the following week. If

both the prior and following week’s values were missing the carry method previously described was applied to the data. Once stage two was completed all missing values for the 400 to 800 pound weight classes were filled. Additionally, only .59% of observations within the data set as a whole were missing. After the process was completed for a third time all missing values within the data set were filled. This concluded all stages of generating values for missing observations and resulted in a data set with no missing data. Following the generation of all missing values within the weekly data, the data was sorted for the first observation within each month. The remaining data that didn’t represent the first observation within each month was then deleted.

Data was sorted before missing observations were filled for the feeder cattle futures data file. To do this, daily contract data was sorted to provide the first observations within each month. The remaining data that didn’t represent the first observation within each month was then deleted.

One stage was required to fill missing contract values in the feeder cattle futures data. If the contract value was present in the original data set this value was used. If the value was missing, the prior and following week’s values were averaged to generate an approximated futures price contract value for the missing observation. When both the prior and following week’s values were missing, another step was utilized. Averages were calculated for each futures contract and were then used to yield carry values (difference between average of one contract and the next contract). These generated carry values were then subtracted from given values in the previous contracts to obtain approximate futures contract values. 4.4% of the observations within the feeder futures data set were adjusted.

After filling blanks in both the cash and feeder futures data sets, these data sets were used to generate actual basis values for each weight class of cattle. To do this appropriate futures price

values were subtracted from cash prices for each weight class and month. To generate expected basis values a three-year historical average method was used according to Tonsor, Dhuyvetter, and Mintert’s paper entitled “Improving Cattle Basis Forecasting” (Tonsor, et al., 2004). For example, if basis values in March of 2000, 2001, and 2002 for 850 pound steers were -$2.00, - $4.00, and -$6.00 the expected basis value for March of 2003 would be -$4.00.