2025/05/08 by Tobias Adrian, Domenico Giannone, Adrian, Tobias +5 · 1 citation
Decision Sciences · #Decision-Making and Behavioral Economics #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Forecasting Techniques and Applications #Methodology (stat.ME) #Risk and Portfolio Optimization
paper · pdf · doi:10.48550/arxiv.2505.05193
openalex publication_date 2025/05/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce methodology to bridge scenario analysis and model-based risk forecasting, leveraging their respective strengths in policy settings. Our 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 supports 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 unexpected circumstances.