Monetary Policy Transmission in Canada – A High Frequency Identification Approach
Bibliographic record
Abstract
Abstract I study the effects of monetary policy shocks in Canada on economic and financial variables. With a narrow window around a policy announcement, I create a new set of intraday level, high-frequency monetary policy surprises using the three-month Canadian Bankers’ acceptance rate futures. I use this measure to identify monetary policy shocks as an external instrument in a monthly VAR. Following a 25 basis point contractionary policy shock, I find that the decline in output is more powerful and peaks earlier than previous empirical works show, with a peak decline of 0.5 % points after 18 months. Price level declines are similarly more powerful and earlier, reaching a decline of 0.3 % points after 24 months. In addition, increases in the credit and mortgage spreads indicate the presence of a domestic credit channel of monetary policy transmission for Canada. Finally, I show that the surprise measure is robust to information effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".