The Monetary Transmission Mechanism in Canada: A Time-Varying Vector Autoregression with Stochastic Volatility
Bibliographic record
Abstract
This study uses the state-space representation of a time-varying vector autoregression with stochastic volatility (TVP-VAR-SV) to study monetary policy and private sector behaviour in Canada. The main results indicate that both shock variances and autoregressive coefficients of the VAR have evolved systematically over time. The time-varying coefficients of the systematic component of the VAR suggest that monetary policy has become more proactive and less reactive regarding inflation since the early-1990s, which coincides with the adoption of explicit inflation targets. Monetary policy is now able to focus mainly on movements in the output gap to prevent future increases in inflation. The coefficients on the policy rate in both the output gap and inflation equations suggest that the private sector and therefore the transmission mechanism have become more sensitive to monetary policy responses. On the other hand, the coefficients on the output gap in the equations for both inflation and the policy rate have been relatively stable over this period, consistent with view that monetary policy remains more forward-looking regarding inflation than being reactive to inflation surprises as in the past.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".