Assessing the US and Canadian neutral rates: 2024 update
Bibliographic record
Abstract
This note presents Bank of Canada staff’s current assessment of the US and Canadian neutral rates of interest. The neutral rate is where the Bank expects the policy rate would settle once output is at its long-run potential level and inflation is at target, after the effects of all cyclical shocks have dissipated (Mendes 2014). The Bank does not target the neutral rate, but this is an important input for its economic projections. We assess both the US and Canadian nominal neutral rates to be in the range of 2.25% to 3.25%, somewhat higher than the range of 2.0% to 3.0% in 2023. The assessed range is back to the level it was at in April 2019. A stronger outlook for potential output growth primarily explains the revision to the US neutral rate (Benmoussa et al. 2024). The revision to the Canadian neutral rate reflects a combination of the revised US neutral rate and key domestic factors, including stronger growth of trend labour input that is fully offset by weaker growth of trend labour productivity over the long term (Devakos et al. 2024). The neutral rate is unobservable and inferred by assessing the evolution of the factors that influence it. Measurements of the neutral rate are subject to a considerable amount of uncertainty. The assessed ranges do not capture the full extent of uncertainties surrounding the neutral rate in the United States and Canada.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".