Bibliographic record
Abstract
In chapter one, I form a new series of monetary policy shocks in Canada using a high-frequency identification approach of a market-based interest rate to capture a monetary policy shock. I then form a monthly series of monetary policy shocks with a 30-day moving average approach consistent with the literature. Applying this as an external instrument in a vector autoregression, the effects of a policy shock are larger than those of previous approaches and there is no price puzzle in the initial impulse response. In chapter two, I examine the non-monetary news component that may exist in Canada’s high-frequency policy surprises. This is important to remove effects contrary to a monetary policy shock. The key to identifying non-monetary news in Canadian policy surprises is considering the monetary process leading into announcements. This suggests splitting announcements into two types to properly account for non-monetary news allowing a robust examination of the non-monetary news in the policy surprises. I focus on the information channel and the response to news channels as explanations of non-monetary news. I find evidence supporting the response to news channel, bolstered by the split announcement type. I form a new series of monetary policy shocks and show that there are no monetary puzzles in the initial impulse responses following a policy shock. I show how this aligns with response to news channel through an illustrative model. This is the first evidence of a response to news channel in monetary policy surprises outside the US. In chapter three, co-authored with Hashmat Khan and Jeremy Kronick, we create a measure of Canada’s neutral rate. We expand beyond the standard short neutral rate and create a neutral rate yield curve, allowing us to discuss the stance of monetary policy in Canada beyond the standard short-term rate. This provides a better discussion about the stance of monetary policy, as monetary policy includes unconventional policy action such as forward guidance, Quantitative Easing and other unconventional methods that target interest rates beyond the short rate on the yield curve. In our original release (October 2024), we suggest Canada continue cutting the key interest rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".