Forward guidance and the private forecast disagreement – case of Poland
Bibliographic record
Abstract
During the period of policy easing in 2013 and prospective tightening in 2017−2019, Narodowy Bank Polski (NBP) applied forward guidance to manage the expectations of market participants. The goal of the policy was to lower the uncertainty related to the future interest rate decisions. We attempt to verify whether the central bank’s communication indeed reduced disagreement, based on the professional forecasters’ survey. We found that the forward guidance introduced in 2013 lowered the perceived interest rate risk. Abandoning the policy in 2014 increased the disagreement in a disproportionately large manner. The reintroduction of the policy in 2017 again allowed to reduce uncertainty. However, it took a year to strengthen its impact. The policy likely prevented an increase of disagreement during the NBP image crisis in late 2018. Our research highlights that it is relatively easy to lose confidence with ill-considered communication, but building credibility requires systematic long work.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".