  Recent studies have analyzed whether one forecast method dominates another
under a class of consistent scoring functions. While the existing literature
focuses on empirical tests of forecast dominance, little is known about the
theoretical conditions under which one forecast dominates another. To address
this question, we derive a new characterization of dominance among forecasts of
the mean functional. We present various scenarios under which dominance occurs.
Unlike existing results, our results allow for the case that the forecasts'
underlying information sets are not nested, and allow for uncalibrated
forecasts that suffer, e.g., from model misspecification or parameter
estimation error. We illustrate the empirical relevance of our results via data
examples from finance and economics.
