Mixing It Up: Inflation at Risk

Abstract Understanding how risk factors shape the economic outlook is essential for guiding policy decisions. This paper develops a flexible framework that decomposes distributional risk forecasts of macro‐economic variables into underlying contributions and supports the construction of interpretable risk measures. Multiple modeling strategies, including density and quantile regression, are accommodated underscoring the versatility of the approach. The framework is illustrated using recent U.S. inflation data, showing postpandemic risk forecasts were driven by the business cycle, commodity prices, monetary policy, and inflation expectations. Simulation studies demonstrate robustness, establishing the framework as a general tool for macro‐economic and financial risk assessment.