Rules versus Discretion in Foreign Exchange Intervention: Evidence from Official Bank of Canada High-Frequency Data
Bibliographic record
Abstract
*Corresponding author. Fatum gratefully acknowledges financial support from a SSHRC Research Grant. We thank the Bank of Canada for providing the official, high-frequency intervention data and Michael M. Hutchison and Barry Scholnick for very helpful comments. We are appreciative of excellent research assistance by Holly Sweeting. The views expressed do not necessarily reflect the views of the Bank of Canada. 2 The Bank of Canada is one of very few central banks that has kept records of the intraday timing of its intervention operations. This paper contains an empirical analysis of a high-frequency data-set of official Bank of Canada intervention data and exchange rates (quoted at the end of every 5-minute interval over a 24-hour period). The data-set covers the January 1995 to September 1998 period and is of particular interest as it spans over two distinctly different intervention regimes- a first regime characterized by purely rules-based (“mechanistic”) intervention versus
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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 | 0.006 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".