The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard VARs. To address these issues, we propose VAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility. The resulting...

VARs are a popular tool for forecasting and structural analysis, but ill-suited to handle occasionally binding constraints, like the effective lower bound on nominal interest rates. We extend the VAR framework by modeling interest rates as censored observations of a latent shadow-rate process,...

We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee's Summary of Economic Projections. At a given point of time, these surveys provide forecasts for macroeconomic variables at multiple...