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Record W2771567740 · doi:10.32796/cice.2016.92.6135

Ratings bancarios y política monetaria

2016· article· es· W2771567740 on OpenAlexaff
Carlos Salvador

Bibliographic record

VenueCuadernos Económicos de ICE · 2016
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsEconomics

Abstract

fetched live from OpenAlex

ResumenEste trabajo analiza el impacto que la política monetaria expansiva mantenida por los bancos centrales a raíz de la crisis financiera ha producido en los ratings de los bancos de la Unión Europea, los Estados Unidos y Japón durante los años 2004-2013.Los reducidos tipos de interés y las escasas diferencias entre los tipos de interés a corto y a largo plazo suponen una amenaza de futuro para el sector bancario.Los resultados obtenidos muestran que el efecto tanto en los tipos de interés, como en la curva de tipos sobre los ratings bancarios tiene forma de U invertida en las tres grandes agencias de calificación Fitch, Moody's y Standard and Poor's.Por tanto, una subida de los tipos de interés, y/o un incremento de la pendiente de la curva de tipos tendría efectos beneficiosos sobre los ratings cuando estos son bajos, pero ulteriores subidas tendrían un efecto menor, agotándose su impacto e incluso pudiendo llegar a ser negativo.Las simulaciones realizadas indican que el efecto máximo de los tipos de interés que se obtiene en niveles del entorno del 2,5 por 100 en Standard and Poor's y del 4,5 por 100 en Moody's y Fitch.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.002

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 head0.021
GPT teacher head0.231
Teacher spread0.210 · 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 designNot applicable
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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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