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Please use this identifier to cite or link to this item: http://purl.umn.edu/99120

Title: Quantile Regression Estimates of Confidence Intervals for WASDE Price Forecasts
Authors: Isengildina-Massa, Olga
Irwin, Scott H.
Good, Darrel L.
Keywords: commodity
evaluating forecasts
government forecasting
judgmental forecasting
prediction intervals
price forecasting
Issue Date: 2010-12
Series/Report no.: JARE
Abstract: 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.
URI: http://purl.umn.edu/99120
Institution/Association: Journal of Agricultural and Resource Economics>Volume 35, Number 3, December 2010
Total Pages: 23
From Page: 545
To Page: 567
Collections:Volume 35, Number 3, December 2010

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