We comprehensively document 1,265 Federal Reserve staff alternative scenarios presented to the Federal Open Market Committee in publicly released materials from 1968 to 2020. Scenarios grew in frequency and sophistication, typically spanning a range of outcomes around the baseline. We construct a taxonomy with six categories: aggregate demand, aggregate supply, external risks, financial conditions, fiscal policy, and expectation shifts. Staff qualitative risk assessments complemented the scenario composition. Comparing scenario forecasts to realized outcomes, the most accurate scenarios often anticipated major macroeconomic developments even when magnitudes were missed, revealing the value and limits of scenario analysis for central bank risk management.

Policy institutions often address uncertainty around a baseline outlook through a limited set of alternative scenarios, rather than specifying a complete predictive distribution. This paper examines optimal policy prescriptions within this setting with a focus on the effect of uncertainty about the outlook. We propose a framework where policymakers form beliefs about the likelihood of a baseline economic path and alternative scenarios, updating these beliefs over time using Bayes’ rule. This “risk-adjusted optimal policy” allows policymakers to set policy rationally while learning which scenario is unfolding. We apply this framework to the U.S. economy at the end of 2024, considering risks of higher inflation and recession around a baseline projection.