From monetary targeting to inflation targeting: lessons from the industrialized countries
Bibliographic record
Abstract
The author examines changes in monetary \n policy in industrial countries by evaluating, and providing \n case studies of monetary targeting, and inflation targeting. \n Inflation targeting has successfully controlled inflation, \n with some qualifications. It weakens the effects of \n inflationary shocks, as examples from Canada, Sweden, and \n the United Kingdom show. It can promote growth, and does not \n lead to increased fluctuations in output. But inflation \n targets do not necessarily reduce the cost of reducing \n inflation. The key to success of inflation targeting, is its \n stress on transparency, and communication with the public. \n Inflation targeting increases accountability, which helps \n ameliorate the time-inconsistency trap (in which the central \n bank tries to expand output, and employment in the short \n run, by pursuing overly expansionary monetary policy). \n Time-inconsistency is more likely to come from political \n pressures on the central bank, to engage in overly \n expansionary monetary policy. A key advantage of inflation \n targeting, is that it helps focus the political debate on \n what a central bank can do in the long run (control \n inflation) rather than what it cannot do (raise economic \n growth, and the number of jobs permanently through \n expansionary monetary policy). By increasing transparency, \n and accountability, inflation targeting helps promote \n central bank independence. Accountability to the general \n public seems to work as well as direct accountability to the \n government. Inflation targeting is consistent with \n democratic principles. In discussing operational design, the \n author explains, among other things, that: 1) Inflation \n targeting is far from rigid rule. 2) Inflation targets have \n always been above zero with no loss of credibility. 3) \n Inflation targeting does not ignore traditional \n stabilization goals. 4) Avoiding undershoots of the \n inflation target, is as important, as avoiding overshoots. \n 5) When inflation is initially high, inflation targeting may \n have to be phased-in after disinflation. 6)The edges of the \n target range, can take on a life of their own. 7) Targeting \n asset prices, such as the exchange rate, worsens performance.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".