Home · Beta · Federal Reserve Survey of Professional Forecasters — GDP / inflation accuracy
Beta · Analyst firms · cited; not independently recomputed
Federal Reserve Survey of Professional Forecasters — GDP / inflation accuracy
- Source class
- Analyst firms
- Metric
- Real-time forecast error vs. final-revised outcome (RMSE per horizon; coverage of probability ranges)
- Reported value
- public — Philadelphia Fed maintains historical SPF data + accuracy reports back to 1968
- Measured
- 2026-04-27
Context
The Federal Reserve Bank of Philadelphia's Survey of Professional Forecasters is the longest-running quarterly survey of US macroeconomic forecasts. The Philadelphia Fed publishes per-horizon forecast accuracy statistics (RMSE for point forecasts; probability-range coverage for binned probability questions like recession in next 4 quarters). Cross-vertical Phase 1 reference for analyst-class calibration.
Citation
Federal Reserve Bank of Philadelphia, Survey of Professional Forecasters — Documentation and Forecast Accuracy.
What Phase 1 launch will add
Calibration Ledger has not independently recomputed the value above. Phase 1 launch (target Q3 2027, gated on prerequisites) will add for this source class:
- Independent recomputation from the original outcome data, under data-licensing agreement
- Time-windowed breakdown (rolling 3-month, 12-month, lifetime)
- Cross-domain calibration (does this source calibrate uniformly across topical verticals?)
- Append-only timestamp anchoring of every score so retroactive revisions are visible
- Per-source citation page with full Murphy decomposition (Reliability − Resolution + Uncertainty)
Other findings in the same source class
- Sell-side equity analysts — earnings forecast accuracy — Systematic optimism + analyst-disagreement-vs-error correlation (proper-scoring-rule analogue for point forecasts)
All other findings
- Good Judgment Project Superforecasters (Human forecasters)
- Metaculus community-prediction aggregate (Forecaster aggregator platform)
- Manifold Markets — platform calibration (Prediction market)
- GPT-4 (OpenAI) — pre-RLHF vs post-RLHF calibration (AI models)
- Open Science Collaboration — psychological science replication rate (Scientific papers)
- Anthropic — Claude / language model self-knowledge (AI models)
- Camerer et al. — social science experiment replication (Nature/Science 2010-2015) (Scientific papers)
- Hausfather et al. — climate model projections vs. observed warming (Scientific papers)
Related
- All beta findings — at-a-glance + JSON + BibTeX exports
- Methodology v1.1 — full Brier + Murphy + append-only framework
- Operator track record — methodology applied to Paulo de Vries’s own dated forecasts
- Source classes — what each of the 6 source classes will score at Phase 1
- Roadmap — milestone status + Q3 2027 launch gate + kill criterion
Last verified: 2026-04-28. Cited; Calibration Ledger has not independently recomputed this finding. Independent recomputation in Phase 1 (Q3 2027). Operator: Paulo de Vries. Contact: contact@calibrationledger.com.