The Effect of Quantitative Easing on the Financial Market in Canada
Bibliographic record
Abstract
This empirical study examines the effects of quantitative easing (QE) on the Canadian financial market. Specifically, the study focuses on the Bank of Canada's Government of Canada Bond Purchase Program (GBPP), conducted during COVID-19 between 2020 and 2022. I use two analytical methods, e.g., event study and time series analysis, to quantify the impact of the Bank of Canada's large asset purchases on the 10-year government bond yield's term premium. The results indicate that the Bank of Canada's $260 billion asset purchases in 2020 would reduce the 10-year term premium by 34 basis points, which suggests the significant impact of quantitative easing on the term premium. Furthermore, the study finds that QE has a portfolio balance effect by reducing the yields on other non-government assets. The findings of this study constitute a significant contribution to the current discourse regarding the use of QE as a monetary policy tool and have implications for future policy decisions related to monetary policy in the Canadian economy.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".