Forward Guidance at the Effective Lower Bound: International Experience
Bibliographic record
Abstract
Forward guidance is one of the policy tools that a central bank can implement if it seeks to provide additional monetary stimulus when it is operating at the effective lower bound (ELB) on interest rates. It became more widely used during and after the global financial crisis. This paper reviews the international experience, based on the six central banks that have used forward guidance when operating at the ELB, in order to assess its effectiveness and the potential risks associated with its implementation. We distinguish between three distinct types of forward guidance (qualitative, time contingent and state contingent) and discuss the channels through which forward guidance operates. Overall, we find that forward guidance can be an effective tool at the ELB when clearly communicated and perceived as credible. Though evidence from the literature is somewhat mixed—since the specific effects vary across economies, episodes and type of guidance—it has generally been found to be effective in (1) lowering expectations of the future path of policy rates, (2) improving the predictability of short-term yields over the near term and (3) changing the sensitivity of financial variables to economic news. However, as with other monetary policy tools, the benefits of forward guidance need to be weighed against the costs. Those costs are mainly associated with potential loss of credibility and increased financial stability risks. Moreover, the international experience with forward guidance under conditions of negative ELBs and interest rates is limited to date.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".