ASSET PRICES AND MONETARY POLICY: A CANADIAN PERSPECTIVE ON THE ISSUES
Bibliographic record
Abstract
The issue addressed in this article is the extent to which monetary policy in Canada should respond to asset-price bubbles. The article concludes that maintaining low and stable consumer price inflation is the best contribution that monetary policy can make to promoting economic and financial stability, even when the economy experiences asset-price bubbles. In extreme circumstances--when an asset-price bubble is well identified and likely to have significant costs to the economy when it bursts--monetary policy might better maintain low and stable consumer price inflation by leaning against a particular bubble even though it may mean that inflation deviates temporarily from its target. Such a strategy might reduce the risk that a crash in asset prices could lead to a recession and to inflation markedly below target in the longer run. The circumstances where this strategy is possible will be rare because economists are far from being able to determine consistently and reliably when leaning against a particular bubble is likely to do more harm than good. Housing-price bubbles should be a greater concern for Canadian monetary policy than equity-price bubbles, since rising housing prices are more likely to reflect excessively easy domestic credit conditions than are equity prices, which are largely determined in global markets.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".