  The macroeconomy is a sophisticated dynamic system involving significant
uncertainties that complicate modelling. In response, decision makers consider
multiple models that provide different predictions and policy recommendations
which are then synthesized into a policy decision. In this setting, we
introduce and develop Bayesian predictive decision synthesis (BPDS) to
formalize monetary policy decision processes. BPDS draws on recent developments
in model combination and statistical decision theory that yield new
opportunities in combining multiple models, emphasizing the integration of
decision goals, expectations and outcomes into the model synthesis process. Our
case study concerns central bank policy decisions about target interest rates
with a focus on implications for multi-step macroeconomic forecasting.
