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 1995/96â€“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.
Volume (Year): 35 (2010)
Issue (Month): 3 (December)
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- Nikolaus Hautsch & Dieter Hess, 2004.
"Bayesian Learning in Financial Markets – Testing for the Relevance of Information Precision in Price Discovery,"
FRU Working Papers
2004/06, University of Copenhagen. Department of Economics. Finance Research Unit.
- 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, Cambridge University Press, vol. 42(01), pages 189-208, March.
- Hautsch, Nikolaus & Hess, Dieter, 2004. "Bayesian learning in financial markets: Testing for the relevance of information precision in price discovery," CFR Working Papers 04-10, University of Cologne, Centre for Financial Research (CFR).
- 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.
- 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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