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INFLATION TARGETING AND THE ECONOMY: LESSONS FROM CANADA'S FIRST DECADE

2001· article· en· W2083156063 on OpenAlexaffabout
C. Freedman

Bibliographic record

VenueContemporary Economic Policy · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of Canada
Fundersnot available
KeywordsEconomicsInflation (cosmology)Monetary policyProductivityInflation targetingRecessionRestructuringPrivate sectorSpillover effectMonetary economicsMacroeconomicsEconomic policyEconomic growthFinance

Abstract

fetched live from OpenAlex

Inflation targeting has become the centerpiece of the monetary policy framework in a number of industrial countries and emerging economies. The first part of this article examines the Canadian experience with inflation targeting since its introduction in early 1991 and various issues that require resolution in establishing such a framework. It also examines the way inflation targets deal with demand, price, and productivity shocks. The second part focuses on Canada's economic performance during the 1990s. Factors other than monetary policy ‐ most notably private sector restructuring and the fiscal situation in the first half of the decade ‐ played an important role in the sluggishness of the recovery from the recession of 1990–91. Trend growth in Canada during the 1990s was lower than in earlier periods and than U.S. trend growth over the same period. The article examines the role of such factors as productivity growth and participation rates in explaining the differences. I conclude that a good monetary policy is necessary but not sufficient for good economic outcomes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score0.784

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0070.003
Scholarly communication0.0080.002
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.056
GPT teacher head0.234
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations12
Published2001
Admission routes2
Has abstractyes

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