Optimal Regime-Switching Density Forecasts

This paper proposes an approach for enhancing density forecasts of non-normal macroeconomic variables using Bayesian Markov-switching models. Alternative views about economic regimes are combined to produce flexible forecasts, which are optimized with respect to standard objective functions of density forecasting. The optimization procedure explores both forecast combinations and Bayesian model averaging. In an application to U.S. GDP growth, the approach is shown to achieve good accuracy in terms of average predictive densities and to produce well-calibrated forecast distributions. The proposed framework can be used to evaluate the contribution of economists' views to density forecast performance. In the empirical application, we consider views derived from the Fed macroeconomic scenarios used for bank stress tests.