Quantile regression estimates of confidence intervals for WASDE price forecasts

Olga Isengildina-Massa, Scott H. Irwin, Darrel L. Good

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)545-567
Number of pages23
JournalJournal of Agricultural and Resource Economics
Issue number3
StatePublished - Dec 2010
Externally publishedYes


  • Commodity
  • Evaluating forecasts
  • Government forecasting
  • Judgmental forecasting
  • Prediction intervals
  • Price forecasting

ASJC Scopus subject areas

  • Animal Science and Zoology
  • Agronomy and Crop Science
  • Economics and Econometrics


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