A Perspective on Inflation Targeting
Bibliographic record
Abstract
One of the more interesting developments in central banking in the past dozen years or so has been the increasingly widespread adoption of the monetary policy framework known as inflation targeting. The approach evolved gradually from earlier monetary policy strategies that followed the demise of the Bretton Woods fixed-exchange-rate system--most directly, I believe, from the practices of Germany's Bundesbank and the Swiss National Bank during the latter part of the 1970s and the 1980s. For example, the Bundesbank, though it conducted short-term policy with reference to targets for money supply growth, derived those targets each year by calculating the rate of money growth estimated to be consistent with the bank's long-run desired rate of inflation, normally 2 percent per year. Hence, the Bundesbank indirectly targeted inflation, using money growth as a quantitative indicator to aid in the calibration of its policy. Notably, the evidence suggests that, when conflicts arose between its money growth targets and inflation targets, the Bundesbank generally chose to give greater weight to its inflation targets (Bernanke and Mihov, 1997). 1 The inflation-targeting approach became more explicit with the strategies adopted in the early 1990s by a number of pioneering central banks, among them the Reserve Bank of New Zealand, the Bank of Canada, the Bank of England, Sweden's Riksbank, and the Reserve Bank of Australia. Over the past decade, variants of inflation targeting have proliferated, with newly industrialized and emerging-market
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".