ABSTRACT Beliefs of professional forecasters are benchmarked against those of a Bayesian econometrician who is learning about the unknown dynamics of the bond risk factors. Consistent with rational Bayesian learning, the forecast errors of individual professionals and are comparably predictable over the business cycle. The secular and cyclical patterns of professionals' forecasts relative to those of are explored in depth. Inconsistent with many models with belief dispersion, the relationship between professionals' yield disagreement and their matched disagreements about macroeconomic fundamentals is very weak.

ABSTRACT We study risk premiums in the U.S. Treasury bond market from the perspective of a Bayesian econometrician who learns in real time from disagreement among investors about future bond yields. Notably, disagreement has substantial predictive power for yields, and 's risk premiums are less volatile than those in the analogous model without learning. 's forecasts are substantially more accurate than the consensus forecasts of market professionals, particularly following U.S. recessions. The predictive power of disagreement is distinct from the (much weaker) one of inflation and output growth. Rather, it appears to reflect uncertainty about future fiscal policy.