Impact des chocs de politiques monétaires sur l’économie canadienne
Bibliographic record
Abstract
This dissertation examines the impact of shocks caused by monetary policies on the following three dependent variables: Canadian GDP, Canadian inflation rate and the Canadian average bank rate. To carry out this study, the local projections of Jordà (2005) are used. The database used is based on that created by Champagne and Sekkel (2017) in their article. The data comes from narrative information then from a realtime monthly database and forecasts on economic projections established by the Bank of Canada for the period ranging from 1974 to 2015. In addition, this dissertation enhances what has been done in the literature by adding US GDP as a control variable. However, because U.S. GDP is not publicly available as a monthly variable, a monthly index of U.S. industrial production and the OECD U.S. GDP indicator are used with data from the Federal Reserve Bank of St. Louis. Once US GDP is added as a control variable, the results vary depending on whether we use the series of shocks without any break, or the one split into parts to account inflation targeting in the 1990’s. For Canadian GDP, the impact is positive for the two series of shocks and with the two control variables. The impact can go up to an increase of 0.5% more than before the addition of the American economy as a control variable. Then, for Canadian inflation, there is not too much impact with the American industrial production index as a control variable, but the OECD indicator affects inflation by around -0.5% for the two series of shocks. Finally, for the average bank rate, there is no significant difference after four years regardless of the shock series and the control variable used. Overall, a similar trend is maintained, but with greater amplitude for the three variables under study. This study enriches the literature on the real impact of monetary policies in Canada as well as on the use of local projections to calculate impulse responses.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".