Vector autoregressions (VARs) with informative steady-state priors are standard forecasting tools in empirical macroeconomics. This study proposes (i) an adaptive hierarchical normal-gamma prior on steady states, (ii) a time-varying steady-state specification which accounts for structural breaks in the unconditional mean, and (iii) a generalization of steady-state VARs with fat-tailed and heteroskedastic error terms. Empirical analysis, based on a real-time dataset of 14 macroeconomic variables, shows that, overall, the hierarchical steady-state specifications materially improve out-of-sample forecasting for forecasting horizons longer than 1 year, while the time-varying specifications generate superior forecasts for variables with significant changes in their unconditional mean.
