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Record W1585357092 · doi:10.7202/800824ar

L’impact de la Banque du Canada sur la disponibilité du crédit bancaire : 1967-1976

2009· article· en· W1585357092 on OpenAlexvenueaboutno aff
Raymond Théoret

Bibliographic record

VenueL Actualité économique · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsMarket liquidityAsset (computer security)Monetary policyBusinessImperfectFinancial systemBalance sheetReserve requirementMonetary economicsEconomicsCentral bankFinance

Abstract

fetched live from OpenAlex

In this article we want to verify if Bank of Canada's actions on the availability of credit were efficient from 1967 to 1976. During this period, the interest rate was chiefly used to maintain balance of payments equilibrium and the Bank of Canada was trying to affect internal credit conditions (here banks' loans) through its effect on the chartered banks' liquid asset ratio. However, this ratio is an imperfect indicator of the availability of banks' credit. Chartered banks have in fact many techniques to obtain liquidities: the whole of these constitutes their liability management mechanism. By this way, they can immune themselves from a restrictive monetary policy which operates via the liquid asset ratio. The degree of accommodation of loans is also another factor to consider when studying the impact of the Bank of Canada on banks' loans. It is evident that a high degree of accommodation of loans is a serious obstacle to monetary policy: liquidity management is probably pushed very far in this case. Our theoretical model takes into account these considerations. And the estimation of this model shows that loans have little reacted to Bank of Canada's actions. The degree of loans accommodation was high and consequently banks checked the Bank of Canada's policy by their liquidity management mechanism.

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)0.001
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.660

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0050.002
Scholarly communication0.0050.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0140.001

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations2
Published2009
Admission routes2
Has abstractyes

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