Scenario Synthesis and Macroeconomic Risk

We develop methodology to bridge scenario analysis and risk forecasting, leveraging their respective strengths in policy settings. The methodology, rooted in Bayesian analysis, addresses the fundamental challenge of reconciling judgmental narrative approaches with statistical forecasting. Analysis evaluates explicit measures of concordance of scenarios with a reference forecasting model, delivers predictive synthesis of the scenarios to best match that reference, and addresses scenario set incompleteness. This provides a framework to systematically evaluate and integrate risks from different scenarios, aiding forecasting in policy institutions while supporting clear and rigorous communication of evolving risks. We also discuss broader questions of integrating judgmental information with statistical model-based forecasts in the face of as-yet unmodeled circumstances.