A0365
Title: Scenario synthesis and macroeconomic risk
Authors: Matteo Luciani - Federal Reserve Board (United States) [presenting]
Domenico Giannone - Johns Hopkins University (United States)
Mike West - Duke University (United States)
Tobias Adrian - Federal Reserve Bank of New York (United States)
Abstract: Methodology is introduced to bridge scenario analysis and model-based risk forecasting, leveraging their respective strengths in policy settings. The Bayesian framework 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 Bayesian predictive synthesis of the scenarios to best match that reference, and addresses scenario set incompleteness. This underlies systematic evaluation and integration of risks from different scenarios, and quantifies relative support for scenarios modulo the defined reference forecasts. The framework offers advances in forecasting in policy institutions that support clear and rigorous communication of evolving risks. Broader questions of integrating judgmental information with statistical model-based forecasts in the face of unexpected circumstances are also discussed.
