Forecasting cotton prices using statistical methods has advantages and disadvantages. The limitations are mainly a matter of data: for structural models, we often don’t have data to model the variables that are really influencing prices. Even if we do have data, our model prediction of price is tied up with the historical set of data that we used to generate the prediction. This can break down when we are in exceptional time periods such as 2010-11.
The advantages of using regression forecasts is that it is a generally acceptable, repeatable, systematic method of measuring the influence of important (or so we hypothesize) variables on cotton prices. For example, we specify that the A-index of world cotton prices is a function of 1) cotton ending stocks-to-use in foreign countries, excluding China, 2) Chinese cotton imports, 3) a dummy variable accounting for the abnormally high price spike of 2011, and 4) a trend variable to capture patterns like population growth and technology improvement. Using annual data between 2000 and formal results of this regression are summarized here:
A quick interpretation of this output is as follows. First, this relationship of the specified independent variables does a fairly decent job explaining most (93%) of the variability in the A-index.
Concerning those variables, the results indicate that world prices move in the opposite direction of world (excluding China) ending stocks-to-use. More specifically, for every 1% increase in world (excluding China) ending stocks, the A-index would be expected to weaken by $0.0066. Chinese imports have a strong positive influence of world prices, with a 0.82 cent increase in the A-index expected from a million bale increase in Chinese imports. Both the price spike dummy variable and the trend variable were also significant and positive.
From a price forecasting standpoint, using the USDA July forecasts of stocks-to-use and Chinese imports, this model predicts an average annual A-index of 83.9 cents per pound for the 2014/15 marketing year. The forecast of 83.9 cents is shown by the black line (on the far right side of the chart). The post estimate is a little high; the A-Index is currently trading about twelve cents lower. The reason for the inaccuracy is likely due to the inclusion of a trend variable. Further, the associated risk bounds imply, unhelpfully, that there is a 95% probability of having the average annual A-index either 27 cents higher or lower than this predicted value. That is what you get for the recent extreme variability in world cotton prices.