Multiple-horizon forecast panels are increasingly used to infer perceived monetary policy rules, but inference depends on how coefficients are pooled across forecasters, dates, and horizons. We treat this pooling structure as the object of inference. In a participant-date-horizon Taylor-rule regression model, we compare pooling patterns using Bayesian marginal likelihoods, applying the framework to the Blue Chip Financial Forecasts, Survey of Professional Forecasters, and the Summary of Economic Projections. The preferred specifications place much of the systematic variation in policy-rate forecasts in intercepts that vary across forecast horizons and survey dates. Evidence of “changing perceptions” of monetary policy via economically meaningful time-varying response coefficients is weak overall.

We develop a finite-horizon planning model in which firms choose how far ahead to plan when setting prices. Planning further ahead improves a firm’s pricing decision but requires cognitive effort. We derive analytical solutions for a firm’s chosen planning horizon and show that large and persistent aggregate demand or supply disturbances induce firms to plan further ahead, making inflation more sensitive to shocks and generating endogenous movements in inflation uncertainty. Quantitatively, we show that the model matches the positive relationship between the size of inflation forecast revisions and inflation uncertainty observed in the data.

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.