Economic uncertainty is a powerful force in the modern economy. Research shows that surges in uncertainty can trigger business cycles, bank runs and asset price  uctuations. But where do sudden surges in uncertainty come from? This paper provides a datadisciplined theory of belief formation that explains large  uctuations in uncertainty. It argues that people do not know the true distribution of macroeconomic outcomes. Like Bayesian econometricians, they estimate a distribution. Our main contribution is to explain why real-time estimation of distributions with non-normal tails are prone to large uncertainty  uctuations. We use theory and data to show how small changes in estimated skewness whip around probabilities of unobserved tail events (black swans). Our estimates, based on real-time GDP data, reveal that revisions in the estimates of black swan risk explain most of the  uctuations in uncertainty.