Reading the Dots: What Fed Rate Projections Reveal and Where They Fall Short

Behind the dots: data, learning, and sentiment in Fed forecasts

How exactly do FOMC members construct their SEP forecasts? Stephen J. Cole’s argues that while the dot plot is one of the Fed’s most closely watched communication tools, very little is publicly known about the internal process behind it. Do members respond rationally to new data, or are their projections also driven by collective optimism and pessimism? Understanding this has direct implications for how seriously markets and policymakers should treat the dot chart as a signal.

Cole assumes each FOMC participant’s forecast is the sum of two parts:

1. A rule-based, data-driven part, where members update their economic outlook as new GDP, inflation, and interest rate data arrives
2. A sentiment part, capturing the residual waves of optimism or pessimism not explained by the data.

He then estimates how much weight members place on recent vs. older data, and how large the sentiment component is, using actual SEP midpoint projections from 2007 to mid-2024.

Findings

• FOMC members do respond to new economic data, but in a gradual, smoothed way as they don’t overreact to any single data release.
• The data-driven component dominates most of the time; sentiment is a secondary factor.
• During recessions (the GFC and COVID-19), sentiment takes over as collective optimism or pessimism drives forecasts more than the data model.
• Pessimists track new data more closely than optimists, who rely more on historical patterns and show persistently positive sentiment on inflation.
• Members consistently overestimate the persistence of inflation, and their beliefs shift most noticeably in and around economic crises.

How Does the FOMC Form Forecasts? An Adaptive Learning Approach Utilizing SEP Data
Author: Stephen J. Cole
From: Marquette University

The Double-Edged Dot: How Fed Projections Inform and Distort Private Expectations

Eric Engstrom, in this paper, examines whether the Fed’s dot plot help or hinder the formation of accurate private-sector interest rate expectations? The motivation is the dot plot’s paradoxical status: it is one of the most market-moving communication tools the Fed has, yet it is only updated quarterly while economic news arrives continuously. This creates a testable tension between the dot plot’s informational value when released and its potential to slow updating once it goes stale.

Engstrom uses Blue Chip Financial Forecasts (survey-based professional forecasts, 1983–2026) and futures-implied market expectations (Eurodollar/fed funds futures, 1986–2026) alongside the SEP median projections (2012–2026). He runs three complementary empirical tests:

1. Forecast accuracy comparison:  Engstrom benchmarks the SEP against a random walk, a real-time VAR model, and market-based measures to confirm the dot plot is genuinely informative.
2. Anchoring regressions: He tests whether private forecast errors are systematically predicted by the gap between current forecasts and the prior quarter’s SEP projection. Under efficient updating, that gap should have no predictive power; if it does, it signals excessive anchoring to stale guidance.
3. Composite anchoring model: He estimates how much of the anchoring is attributable to the prior consensus forecast vs. specifically to the lagged SEP, decomposing the two effects.

Findings

• The dot plot beats the alternatives: In the post-2012 period, the median SEP achieves the lowest forecast error at the one-quarter horizon among all methods tested, outperforming Blue Chip surveys, VAR models, and futures markets.
• The effect strengthens with horizon: Anchoring is modest at one quarter but becomes very large at three to four quarters, where the model explains 25–40% of forecast error variance.
• Both survey and market forecasts are affected: The anchoring effect is strongest and most robust in Blue Chip surveys; it is present but weaker in futures-implied rates, where macro-financial controls absorb part of the signal.
• The 2022 tightening cycle is the key case study: In December 2021, the dot plot projected a gradual rate path. By early 2022, incoming inflation data and Fed communications pointed to much faster tightening yet both survey and market forecasts adjusted only slowly away from the December SEP benchmark, contributing to systematic underestimation of rate increases. Engstrom estimates that reliance on the stale dot plot materially slowed the upward adjustment of forward rates.
• The core policy insight: Official guidance can simultaneously improve average forecast accuracy (a static benefit) and slow the incorporation of new information (a dynamic cost). The quarterly publication frequency of the SEP is itself central to this trade-off.

Anchored to the Dot Plot: Central Bank Projections and Interest Rate Expectations
Author: Eric Engstrom
From: Federal Reserve Board