Determinants of the long-term yield in Canada: an open economy VAR approach
Why this work is in the frame
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Bibliographic record
Abstract
This study analyses the economic determinants of short- and long-term interest rates in Canada using a structural vector autoregressive (VAR) model. The VAR takes into consideration that Canadian financial markets are small and open relative to those in the USA and that Canada is a relatively large exporter of commodities. In part, the empirical results for Canada are similar to those for the USA. Aggregate demand shocks have relatively large and persistent effects on long-term yields, while aggregate supply shocks do not have significant effects. However, monetary policy shocks in Canada are found to have larger and more persistent effects on long-term yields than those found for the USA. The most striking result is that movements in US monetary policy have relatively large, significant and persistent effects on Canadian long-term bond yields. Furthermore, US monetary policy disturbances can account for the overall trend in long-term yields in Canada.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it