Inflation targeting and private sector forecasts
Bibliographic record
Abstract
Introduction Transparency is one of the biggest innovations in central bank policy of the past quarter-century. Modern central bankers believe that they should be as clear about their objectives and actions as possible. This notion arises from the view that policymakers should be a source of stability, not a source of noise, with the economy and markets responding to data and not to the policymakers themselves. Inflation targeting is one of the first and most comprehensive implementations derived from this view. As a framework for monetary policy, inflation targeting involves ‘the public announcement of medium-term numerical targets for inflation [and] increased transparency of the monetary policy strategy through communication with the public and the markets about plans, objectives, and decisions of the monetary authority’ (Mishkin 2002: 361). The result is not just clearly understood and published numerical targets, but also inflation reports that explain past and likely future actions. Most economists believe that greater transparency is beneficial (see, for example, the surveys by Geraats 2002, Carpenter 2004, Dincer and Eichengreen 2007 and Walsh 2007). Transparency is not nudity, however. Understanding policymakers' contingency plans does not mean laying the policymaking process bare for all to see. Monetary policymakers should not put cameras in the meeting room. There are clear limits. What are they?
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".