Can More Public Information Raise Uncertainty? The International Evidence on Forward Guidance
Bibliographic record
Abstract
Central banks have used different types of forward guidance, where the forward guidance horizon is related to a state contingency, a calendar date or left open-ended. This paper reports cross-country evidence on the impact of these different types of forward guidance on the sensitivity of bond yields to macroeconomic news, and on forecaster disagreement about the future path of interest rates. We show that forward guidance mutes the response to macroeconomic news in general, but that calendar-based forward guidance with a short horizon counterintuitively raises it. Using a model where agents learn from market signals, we show that the release of more precise public information about future rates lowers the informativeness of market signals and, as a consequence, may increase uncertainty and amplify the reaction of expectations to macroeconomic news. However, when the increase in precision of public information is sufficiently large, uncertainty is unambiguously reduced. JEL Classification: D83, E43, E52, E58
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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 | 0.010 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".