  We propose an approach for generating macroeconomic density forecasts that
incorporate information on multiple scenarios defined by experts. We adopt a
regime-switching framework in which sets of scenarios ("views") are used as
Bayesian priors on economic regimes. Predictive densities coming from different
views are then combined by optimizing objective functions of density
forecasting. We illustrate the approach with an empirical application to
quarterly real-time forecasts of U.S. GDP growth, in which we exploit the Fed's
macroeconomic scenarios used for bank stress tests. We show that the approach
achieves good accuracy in terms of average predictive scores and good
calibration of forecast distributions. Moreover, it can be used to evaluate the
contribution of economists' scenarios to density forecast performance.
