A BVAR Analysis on Channels of Monetary Policy Transmission in Brazil
Bibliographic record
Abstract
This article measures the responses of GDP and inflation to a positive shock of the variables that make up the channels of transmission of monetary policy. The results of impulse-response functions of the estimated Bayesian VAR (BVAR) were: an increase in the short-term interest rate (SELIC) leads to a long-term interest rate increasing and consequently a reduction in GDP. Free credit does not have a significant impact on Brazilian GDP, given the low free credit/GDP ratio (Bogdanski et al., 2000). A shock in inflation expectations result in a decreasing trajectory of GDP, a fact consistent with the Fisher effect (Mishkin, 2009); and a shock at SELIC reduces inflation in the first two months, there is no “price puzzle”. A credit shock does not cause significant pressures on inflation. The Inflation does not show a well-defined time path after a shock in asset prices. The decomposition of the variance of the forecast error, in turn, showed that: GDP, in the short term, has its forecast errors explained by its own shocks, 70% on average. However, in the medium term, their forecast errors are explained by their own shocks, around 35%, by inflation shocks, 34%, and by interest rate shocks, 20%. The other transmission channels do not have, in the short and medium terms, significant influence on GDP forecast errors, except the asset prices; and inflation forecast errors are explained, in the short term, mainly by their own shocks, 85% on average. In the medium term, inflation forecast errors are explained 68% by inflation itself, 6% by GDP and the others transmission channels participate individually, with approximately 6%. These results are robust when controlled for commodity prices.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".