Oferta de crédito público e saldo de perdas ante um cenário de choques políticos-intitucionais, econômicos e sanitários: um estudo de caso a partir do Banco do Brasil
Bibliographic record
Abstract
This paper shows the importance of credit supply for economic growth and evaluates the relationship between this variable and the bank's loss balance in a scenario of politicalinstitutional, sanitary and sanitary shocks. For this purpose, an empirical study is carried out in a time series between the first quarter of 2002 and the first quarter of 2022, with data from two Banco do Brasil portfolios. The estimates would allow us to infer that, on a quarterly basis, there is no seasonality in relation to the losses of the Loan Portfolio, as well as this balance of losses is mainly influenced by the size of the Portfolio, and may increase by 2% to 3% for each BRL 10,000.00 (ten thousand reais) additional. Reducing the complete sample to the pre-covid and pre-impeachment subperiods showed that, until the pandemic, there was no loss of statistical significance in the size of the Portfolio as an explanation factor for the balance of losses. It was also found that in the post-impeachment period there is no relationship between the loan portfolio and the balance of losses, which suggests that other factors had more impact in the period. The results suggest the need to ensure an optimal size for the volume of the credit portfolio, under penalty of incurring greater losses in periods of high instability.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".