How much do financial analysts disagree on the future path of the ECB's interest rate?
Bibliographic record
Abstract
On 6 June 2024, the European Central Bank (ECB) lowered the main refinancing operations (MRO) rate from 4.5% to 4.25%. This decision followed a period of elevated interest rates intended to combat high inflation in the euro area, which peaked at 10.6% in October 2022 in the aftermath of the COVID-19 pandemic and the Russian invasion of Ukraine. Although inflation levels are now closer to the ECB's medium-term target of 2%, some doubt remains on whether the inflationary pressure has truly abated, since the last mile of the inflation cycle is often perceived as challenging. Indeed, inflation increased again in May 2024 to 2.6%, from 2.4% in April, and the ECB has not committed itself to a certain interest rate path. Instead, it follows a data-dependent meeting-by-meeting approach for further interest rate decisions. In this policy brief, we analyse whether and how much professional forecasters and market analysts disagree on the nature and speed of future interest rate decisions by the ECB. We also consider the role of uncertain dynamics of future inflation and the economic recovery in the euro area to explain the dispersion of interest rate expectations. For this purpose, we asked the participants in the June 2024 wave of the ZEW's Financial Market Survey for their expectations regarding interest rate decisions at upcoming Governing Council meetings. We condition the individual responses on the respondents' short- and medium-term inflation and GDP growth expectations and supplement our findings with similar evidence for the US.
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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 | 0.019 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".