Scenario Synthesis and Macroeconomic Risk

TL;DR

This paper presents a Bayesian framework that combines scenario analysis with statistical risk forecasting to improve policy risk assessment and communication, addressing scenario set incompleteness and integrating judgmental and model-based insights.

Contribution

It introduces a novel Bayesian methodology that synthesizes scenario analysis with model forecasts, enhancing risk evaluation and communication in policy settings.

Findings

Framework effectively reconciles judgmental scenarios with statistical models.

Addresses scenario set incompleteness systematically.

Supports clear communication of evolving risks.

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.