Greater Transparency in Monetary Policy: Impact on Financial Markets
Bibliographic record
Abstract
Measures have been taken by the Bank of Canada to increase the transparency of Canadian monetary policy. This paper examines whether the greater transparency has improved financial markets' understanding of the conduct of monetary policy. In theory, it should result in reduced conditional uncertainty because investor expectations would be formed with a superior information set. The market's response to releases of the Bank of Canada's Monetary Policy Report and to changes in the Bank's operating band for the overnight interest rate is examined. The empirical results suggest that the Bank's efforts at increasing transparency appear to have helped market participants anticipate pending monetary policy actions. Indeed, the amount of uncertainty that surrounds the Bank's actions is now broadly consistent with that reported for other major countries. The issue of whether there should be limits on the amount of transparency in the conduct of monetary policy is also explored. The paper concludes that there is possibly some merit in the Bank's providing more frequent information on its economic outlook and highlighting the uncertainty that surrounds the Bank's views. However, the paper argues against publishing the detailed results of the Bank's economic projections. It also notes that the element of surprise can be useful on occasion with respect to the Bank's operations in financial markets.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".