Risks and Uncertainty in Monetary Policy

2026-06-15

AI Summary

This study addresses the central bank’s pressing need to effectively integrate highly interpretable yet non-probabilistic narrative scenarios with probabilistic but less interpretable distributional forecasts when assessing macroeconomic risks under deep uncertainty. The authors propose a scenario synthesis approach that, within a unified probabilistic framework, treats both baseline projections and alternative scenarios as conditional predictive densities. By constructing a reference distribution from distributional forecasts and combining it with Bayesian weight allocation, the method coherently fuses these distinct forecasting tools. This work represents the first systematic integration of narrative scenarios and probabilistic forecasts in a complementary manner, offering monetary policymakers a structured, operational, and reproducible risk assessment mechanism that significantly enhances both the consistency and practical utility of macroeconomic risk evaluations.

Abstract

Central banks monitor macroeconomic risk through two traditions: scenario analysis, regularly used since the mid-1990s, and distributional forecasting, practiced since the late 1960s. The two are complementary but separate: scenarios provide narratives without probabilities, while predictive distributions provide probabilities with limited economic interpretation. Treating baseline forecasts and scenarios as conditional predictive densities, and distributional forecasts as reference predictive distributions, places both within a common framework and clarifies their roles. The Scenario Synthesis assigns weights to scenarios consistent with the reference distribution, offering a practical and reproducible tool for risk assessment and policy deliberation under deep uncertainty.

Problem

• monetary policy
• risk assessment
• scenario analysis
• distributional forecasting
• deep uncertainty

Innovation

• Scenario Synthesis
• predictive distributions
• monetary policy
• risk assessment
• deep uncertainty