Quantile Regression Estimates of Confidence Intervals for WASDE Price Forecasts
AbstractThis 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 1995/96â€“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.
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Bibliographic InfoArticle provided by Western Agricultural Economics Association in its journal Journal of Agricultural and Resource Economics.
Volume (Year): 35 (2010)
Issue (Month): 3 (December)
commodity; evaluating forecasts; government forecasting; judgmental forecasting; prediction intervals; price forecasting; Crop Production/Industries; Demand and Price Analysis;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Hautsch, Nikolaus & Hess, Dieter, 2007.
"Bayesian Learning in Financial Markets: Testing for the Relevance of Information Precision in Price Discovery,"
Journal of Financial and Quantitative Analysis,
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- Nikolaus Hautsch & Dieter Hess, 2004. "Bayesian Learning in Financial Markets: Testing for the Relevance of Information Precision in Price Discovery," Discussion Papers 04-17, University of Copenhagen. Department of Economics.
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- Taylor, James W. & Bunn, Derek W., 1999. "Investigating improvements in the accuracy of prediction intervals for combinations of forecasts: A simulation study," International Journal of Forecasting, Elsevier, vol. 15(3), pages 325-339, July.
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