Uncertainty, Information, and Disagreement of Economic Forecasters

An information framework is proposed for the systematic application, unification, and generalizations of the measures used in the uncertainty and disagreement of economic forecasters literature. The framework uses the mixture model of density forecasts and applies an uncertainty function and an information divergence to individual forecast distributions generated in a random environment and to their mixture distribution. The focal measure is the Jensen-Shannon divergence of the mixture which admits Kullback-Leibler and mutual information representations. Illustrations include exploring the dynamics of the individual and aggregate uncertainty about the US inflation rate using the Survey of Professional Forecasters (SPF). Each quarter the SPF solicits forecast distributions on a set of intervals and the survey design has been changed over time. We show that the normalized entropy index corrects the distortion caused by the changes made in the number of intervals. Bayesian hierarchical models are used to examine the association of the inflation uncertainty with the anticipated inflation and the dispersion of point forecasts. Implementation of the information framework based on variance and Dirichlet model for capturing uncertainty about the probability distribution of the economic variable are briefly discussed