Forward Guidance 101B: A Roadmap of the International Experience
Bibliographic record
Abstract
Forward guidance is a communication tool that allows central banks to convey their future monetary policy actions, conditional on their evaluation of the economic outlook. We explained in Contessi and Li (2013) the economic rationale of this policy, as well as its different types and the experience with forward guidance in the United States. The Federal Reserve is just one of several central banks that have adopted forward guidance since the beginning of the financial crisis and in an environment of near-zero policy rates (zero lower bound). Others include the Bank of Canada, Bank of England (BOE), the Czech National Bank, and the European Central Bank (ECB). In 1999, Japan became the first major central bank to adopt policy statement language that would become fairly typical of forward guidance when the policy rate was also near zero. In addition to the recent adoption of forward guidance by these central banks, other inflation-targeting banks have adopted forward guidance in an environment without the zero lower bound. These banks, typically in small, open economies, include those in New Zealand, Norway, and Sweden (Andersson and Hoffman, 2009; Kool and Thornton, 2012). We synthetize the experience of both groups of countries depending on whether they adopted forward guidance during times of conventional monetary policy with policy rates above zero (see the first table) or when the policy rate was near zero (see the second table). The Reserve Bank of New Zealand (RBNZ) has the longest history of providing forward guidance about future monetary policy. The RBNZ began publishing a path for its monetary condition index and forecast for the inflation
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".