Why is Canada's Price Level so Predictable?
Bibliographic record
Abstract
One of the pioneers of inflation targeting (IT), the Bank of Canada is now considering a possibility of switching to price-level-path targeting (PLPT), where past deviations of inflation from the target would have to be offset in the future, bringing the price level back to a predetermined path. This paper draws attention to the fact that the price level in Canada has strayed little from the path implied by the two percent inflation target since its introduction in December 1994, and has tended to revert to that path after temporary deviations. Econometric analysis using Bayesian estimation suggests that a low probability can be assigned to explaining this behavior by sheer luck manifesting itself in mutually offsetting shocks. Much more plausible is the assumption that inflation expectations and interest rates are determined in a way that is consistent with an element of PLPT. This suggests that the difference between IT as it is actually practiced (or perceived) and PLPT may be less stark than what pure theoretical constructs posit, and that the transition to a fullfledged PLPT regime will likely be considerably easier than what was previously thought. The paper also shows that inflation expectations are a major driver of actual inflation in Canada, which makes it easier to keep inflation close to the target without large output costs.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".