Accuracy of USDA forecasts of corn ending stocks
In addition to the general pattern of errors, there are other important aspects of forecast errors. One important aspect of the forecasts of ending stocks is whether the forecasts are unbiased. That is, are the average errors near zero? As can be seen in Figure 1, the monthly forecast errors do not center on zero, with a tendency towards under-estimation of final ending stocks in 15 of the 17 months. The magnitude of the monthly bias is generally small, less than 50 million bushels for 10 of the 17 months. The largest bias is associated with August (+85 million bushels) and September (+95 million bushels) forecasts in the year of harvest. None of the monthly bias calculations are statistically different from zero. So, from a statistical standpoint the ending stocks forecasts in all months are unbiased.
A second important aspect of the forecast errors is the source of the errors. Errors are the result of errors in the forecast of other elements of the corn balance sheet. Throughout the forecast cycle, there may be errors in forecasting corn consumption in each of the three major categories of feed and residual use, food, seed, and industrial use, and exports. In addition, errors in the forecast of crop size may be present from May before harvest through December after harvest since the USDA final production estimate is not released until January after harvest. Finally, errors in the estimate of stocks at the beginning of the marketing year may be present from May before harvest through September of the harvest year since USDA's September 1 stocks estimate is not released until the end of September. The estimated magnitude of imports may also be a source of error in the forecast of ending stocks, but since that is typically a small amount it was not considered in this analysis.
click image to zoom Figure 2 summarizes the analysis of the source of forecast errors for year ending stocks. For each of the five balance sheet items, a simple correlation is calculated between the forecast error for ending stocks and the forecast error in that balance sheet item for each month during the forecast cycle (+1 perfect positive correlation; -1 perfect negative correlation). As indicated, the highest correlations are for production forecast errors, export forecast errors, and feed and residual forecast errors. As expected, errors in production forecasts dominate early in the forecast cycle and errors in feed and residual forecasts dominate late in the forecast cycle. At first glance, the pattern of positive correlations for all balance sheet categories early in the forecasting cycle, except food, seed, and industrial, may seem odd. This actually makes sense since production and usage errors will tend to be positively correlated before harvest. If a large production forecast is made then larger usage forecasts will also tend to be made and vice versa. This linkage is cut once the size of the crop is determined.
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