The Value of Information for Crop Forecasting with Bayesian Speculators: Theory and Empirical Results

This paper develops a model of the way in which information about current conditions is translated by Bayesian speculative inventory holders into forecasts of the flow of harvests, and in turn, into forecasts of prices in a competitive market system. Improvement in the information system (e.g., more accurate observations) affects the commodity price distribution, and this change can be evaluated using Marshallian surplus from consumption. The model is applied empirically to the case of wheat crop forecasting in the United States.