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Record W7097734612

A Perspective on Inflation Targeting

2003· article· en· W7097734612 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEuropean Monetary and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsMonetary policyInflation targetingDemiseInflation (cosmology)Money supplyDozenCentral bankPerspective (graphical)
DOInot available

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.028
GPT teacher head0.213
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2003
Admission routes1
Has abstractyes

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