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.

翻译：宏观经济是一个复杂的动态系统，涉及显著的不确定性，这使得建模工作变得复杂。为此，决策者会考虑多个模型，这些模型提供了不同的预测和政策建议，随后被综合成一个政策决策。在此背景下，我们开发了贝叶斯预测决策综合（BPDS）来形式化货币政策决策过程。BPDS借鉴了模型组合和统计决策理论的最新进展，这些进展为组合多个模型提供了新的机遇，强调将决策目标、预期和结果整合到模型综合过程中。我们的案例研究关注中央银行关于目标利率的政策决策，重点探讨其对多步宏观经济预测的影响。该应用也推动了条件预测和BPDS方面的新方法论发展，这些内容在本文中得以呈现和阐述。