Quantile Regression Estimates of Confidence Intervals for WASDE Price Forecasts
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This study uses quantile regressions to estimate historical forecast error distributions for
WASDE forecasts of corn, soybean, and wheat prices, and then compute confidence
limits for the forecasts based on the empirical distributions. Quantile regressions with fit
errors expressed as a function of forecast lead time are consistent with theoretical forecast
variance expressions while avoiding assumptions of normality and optimality. Based on
out-of-sample accuracy tests over 1995/96–2006/07, quantile regression methods produced
intervals consistent with the target confidence level. Overall, this study demonstrates that
empirical approaches may be used to construct accurate confidence intervals for WASDE
corn, soybean, and wheat price forecasts.