Transparency and the Response of Interest Rates to the Publication of Macroeconomic Data
Bibliographic record
Abstract
The benefits of transparency - the outcome of the measures taken by the central bank to allow financial markets and economic agents to understand the factors it takes into account in formulating monetary policy - are now widely recognized. These benefits include smoother implementation of monetary policy and increased effectiveness as markets improve their ability to anticipate the Bank's policy decisions and account for them in their operations. How interest rates respond to the publication of macroeconomic data depends on the degree of transparency in monetary policy, as the rates will rise or fall as a reflection of the market's revised expectations. Before the Bank of Canada adopted initiatives to improve transparency, such as the inflation-control targets, the semi-annual publication of the Monetary Policy Report and Updates, and the fixed announcement dates, changes to the overnight rate created some volatility in interest rates, and publishing Canadian macroeconomic data did not appear to have a major impact on rates. This article shows how the Bank of Canada's steps towards greater transparency have increased the impact of Canadian data on short-term interest rates and have improved financial markets' understanding of how monetary policy decisions are taken.
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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 | 0.048 | 0.269 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".