Scenario Synthesis and Macroeconomic Risk

Tobias Adrian, Domenico Giannone, Matteo Luciani and Mike West ()
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Mike West: https://stat.duke.edu/research/west

No 2025-036, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: 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.

Keywords: Macroeconomic Forecasting; Mixtures of Scenarios; Misclassification Rates; Entropic Tilting; Bayesian Predictive Synthesis; Judgmental Forecasting; Forecast Risk Assessment (search for similar items in EconPapers)
Pages: 29 p.
Date: 2025-05-20
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https://www.federalreserve.gov/econres/feds/files/2025036pap.pdf (application/pdf)

Related works:
Working Paper: Scenario Synthesis and Macroeconomic Risk (2025)
Working Paper: Scenario Synthesis and Macroeconomic Risk (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2025-36

DOI: 10.17016/FEDS.2025.036

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