Estimating the Appropriate Quantity of Settlement Balances in a Floor System
Bibliographic record
Abstract
In April 2022, the Bank of Canada announced that it would continue to use a floor system to implement monetary policy by providing a sufficiently large quantity of settlement balances to enable the overnight repo rate to trade at close to the deposit rate. In contrast, the Bank’s guiding principles of prudence, transparency and neutrality, which govern the management of its balance sheet, favour maintaining settlement balances as low as possible. In this context, this paper describes two complementary approaches to estimating the appropriate quantity of settlement balances needed to effectively maintain a floor system. The first is a regression-based analysis to estimate the quantity required to maintain the overnight repo rate close to the Bank’s policy interest rate (which is equal to the deposit rate in a floor system). The second is an analysis of operational considerations in implementing a floor system in Canada. Both approaches highlight that considerable uncertainty exists in determining the demand for settlement balances. Such uncertainty emphasizes the need for the Bank to monitor money market conditions as it continues to normalize its balance sheet after undertaking quantitative easing operations related to the COVID-19 pandemic.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".