Scenario Synthesis and Macroeconomic Risk

2025-05-08

AI Summary

This paper addresses three core challenges in policy risk forecasting: the difficulty of integrating narrative scenario analysis with quantitative statistical models (e.g., DSGE or VAR), incompleteness of scenario sets, and the lack of rigorous quantification of scenario support. To this end, we propose a novel Bayesian协同 framework featuring three innovations: (1) a formal measure of scenario–model consistency; (2) Bayesian predictive synthesis that coherently integrates expert-judgment-based scenarios with structural econometric models; and (3) probabilistic calibration and synthetic completion of incomplete scenario sets. The method substantially enhances forecast robustness and interpretability, enabling explicit quantification of relative scenario support, hierarchical representation of uncertainty, and dynamic risk communication across multiple scenarios. It delivers a theoretically rigorous yet operationally practical paradigm for policy risk assessment.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Game Theory and Economic Paradigms: Cooperative Game Theory

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Bridging scenario analysis and model-based risk forecasting
• Reconciling judgmental narrative with statistical forecasting
• Evaluating and integrating risks from incomplete scenario sets

Innovation

• Bayesian framework reconciles narrative and statistical forecasting
• Evaluates scenario concordance with reference forecasting model
• Synthesizes scenarios to match reference model predictions