Decision synthesis in monetary policy

The macroeconomy is a complicated dynamic system with significant uncertainties that make modelling difficult. Consequently, decision-makers consider multiple models that provide different predictions and policy recommendations and then synthesize that information into a policy decision. We use Bayesian predictive decision synthesis (BPDS) as a way formalize this monetary policy decision-making process. BPDS draws on recent developments in model combination and statistical decision theory that make it possible to combine models in a manner that incorporates decision goals, expectations and outcomes. We develop a BPDS procedure for a case study of monetary policy decision-making with an inflation-targeting central bank and compare the results against standard model-combination approaches.