Macroeconomic Effects of Central Bank Transparency: The Case of Brazil
Bibliographic record
Abstract
Nowadays there is a tendency for central banks to increase trans-parency in the conduct of monetary policy. Central bank transparen-cy could be defined as the existence of symmetric information between monetary policymakers and other economic agents. High degrees of transparency reduce uncertainty, improve the private-sec-tor inference about central bank goals, and increase the effectiveness of monetary policy. There is now an increasing literature that meas-ures the effects of transparency on average inflation, output volatility (Chortareas, Stasavage, and Sterne 2002), the efficiency of monetary policy (Cecchetti and Krause 2002), and the volatility of financial markets (Ehrmann and Fratzscher 2005). Some empirical analysis highlights the advantages of transparency due to a fall in asymmetric information. Siklos (2000) analyzes the impact of Canadian central bank transparency on the uncertainty of financial economic agents through a change in kurtosis of some financial assets for different periods. The analysis of kurtosis is made around dates of changes in the basic interest rate and the publication of the bank’s Inflation Report. Furthermore, Siklos subdivides the period under analysis taking into consideration the introduction of
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".