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Record W2756989940 · doi:10.33834/bkr.v3i1.94

Impacto de choques de política monetária sobre a oferta de crédito regional: Uma análise econométrica a partir da metodologia VAR para o Brasil nos anos 2000

2017· article· pt· W2756989940 on OpenAlexaff
Leonardo Dondoni Dutra, Carmem Feijó, Júlio Cesar Albuquerque Bastos

Bibliographic record

VenueBrazilian Keynesian Review · 2017
Typearticle
Languagept
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsPolitical scienceEconomicsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

O objetivo deste trabalho é investigar empiricamente o impacto da política monetária na oferta de crédito nas regiões, partindo da hipótese de que este impacto será diferenciado pois depende da estratégia de gestão de ativos da firma bancária em cada região. Desta forma, este trabalho investiga primeiramente as diferentes reações das firmas bancárias a nível regional, quando da ocorrência de choques de política monetária a variáveis de comportamento mapeadas pela preferência pela liquidez bancária e pelo nível de incerteza tomado como proxy das provisões de operações de crédito. Em seguida, investiga os impactos da política monetária sobre as diferentes maturidades do crédito bancário regional. Conclui que a política monetária causa impactos diferenciados na oferta de crédito ao nível das regiões brasileiras verificados pela atuação diferenciada do comportamento de gestão bancária comprometendo assim a possibilidade desse setor em se tornar o elemento proeminente do processo de indução ao crescimento econômico e de redução das desigualdades regionais.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.368
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.005

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 head0.080
GPT teacher head0.335
Teacher spread0.255 · 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; both teacher heads agree on what is shown here.

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".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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