Quantile Regression Estimates of Confidence Intervals for WASDE Price Forecasts

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.


Issue Date:
2010-12
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/99120
Published in:
Journal of Agricultural and Resource Economics, Volume 35, Number 3
Page range:
545-567
Total Pages:
23
Series Statement:
JARE




 Record created 2017-04-01, last modified 2017-08-25

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