Decision synthesis in monetary policy

TL;DR

This paper introduces Bayesian predictive decision synthesis (BPDS) to improve monetary policy decisions by integrating multiple models, decision goals, and outcomes, with a focus on central bank interest rate targeting and macroeconomic forecasting.

Contribution

The paper develops a novel BPDS framework that formalizes the decision-making process in monetary policy, combining model predictions and decision criteria in a Bayesian setting.

Findings

BPDS effectively synthesizes multiple models for policy decisions.

Application to central bank interest rate setting demonstrates improved forecasting.

Methodological advances in conditional forecasting are presented.

Abstract

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 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. This application also motivates new methodological developments in conditional forecasting and BPDS, presented and developed here.