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Record W2069995838 · doi:10.5430/afr.v2n4p104

Evaluation of the European Central Bank’s Monetary Policy in Terms of Taylor Rule

2013· article· en· W2069995838 on OpenAlexvenueno aff
Scântee Roxana, Ovidiu Stoica

Bibliographic record

VenueAccounting and Finance Research · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
FundersEuropean Social Fund
KeywordsTaylor ruleEconomicsMonetary policyInflation (cosmology)European unionInflation targetingOutput gapInterest rateMonetary economicsEuropean monetary unionPoint (geometry)Order (exchange)Gross domestic productMacroeconomicsInflation rateInternational economicsCentral bankFinance

Abstract

fetched live from OpenAlex

The purpose of this paper is to analyze, using Taylor rule, the impact of European Union (EU) enlargement on national banking systems and the optimal monetary policy interest rate in an ideal monetary union composed by all 27 EU states. We employ the inflation, Gross Domestic Product (GDP) and the GDP forecast from EU 27 for the 2001-2011 period. Considering the pillars mentioned before, namely inflation and GDP, the monetary policy from the Euro Area may be described by a rule which uses gap on both, inflation and output. A starting point is the Taylor rule (1993), whose main idea is that central banks react to inflation deviations from target levels. We evaluate the impact of the economic environment and its deviations on national banking systems and on banking competition. The results are included in the trend of the previous research. Interest rate estimation using Taylor rule for European Union countries led to a significant difference, in line with current evolutions and disparities. This article contributes to the scientific knowledge both with a longer analyzed period and also with a review that includes all the countries from EU.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.070
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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)0.0000.000

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 head0.121
GPT teacher head0.307
Teacher spread0.186 · 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.

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
Published2013
Admission routes1
Has abstractyes

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