A look back at 25 years of the ECB SPF
Bibliographic record
Abstract
This paper looks back on the 25-year history of the ECB Survey of Professional Forecasters (SPF). Since its launch in the first quarter of 1999, it has served as an important input for policymaking and analysis, especially over the past five years, where the euro area has, following a period of low inflation, navigated a global pandemic, Russia's invasion of Ukraine and an unprecedented surge in inflation. The survey has evolved over time and provides not only a long time series of economic expectations and forecasts, but also valuable insights on key topical issues and on economic risks and uncertainties. We show that, for each of the three main macroeconomic variables forecast - HICP inflation, real GDP growth and the unemployment rate - the track record of the ECB SPF in forecasting has been broadly comparable to that of the Eurosystem. In addition, its combination of quantitative point forecasts and probability distributions with qualitative explanations has provided useful input for macroeconomic analysis. Beyond analyses of the forecasts for the main macroeconomic variables, there are also two further sections that examine the technical assumptions (oil prices, policy rates, exchange rates and wages) underlying SPF expectations and an analysis and assessment of measures of macroeconomic uncertainty. Technical assumptions are shown to account for the lion's share of the variance in the inflation forecast errors, while uncertainty is shown to have increased considerably relative to that which prevailed during the early years of the SPF (1999-2008). Looking ahead, the SPF - with its long track record, its large and broad panel (spanning both financial and non-financial forecasters) and committed panellists - will undoubtedly continue to provide timely and useful insights for the ECB's policymakers, macroeconomic experts, economic researchers and the wider public.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".