Should Canadian monetary policy respond to asset prices? Evidence from a structural model
Bibliographic record
Abstract
Although the Bank of Canada admits asset prices are considered in its policy deliberations because of their effects on inflation or output gap, the Bank of Canada denies trying to stabilize asset prices around fundamental values. However, since the start of the Bank of Canada we have seen a boom as well as a bust in the stock market. Are we to believe that the Bank of Canada did not react to these stock market fluctuations, apart from their impact consequences on economy? We investigate this issue by using a structural model based on the New Keynesian framework that is augmented by a stock market variable. We use an econometric method that allows us to distinguish the direct effect of stock prices on Bank of Canada policy rates from indirect effects via inflation or GDP. Our results suggest that stock market stabilization plays a larger role in Bank of Canada interest rate decisions than it is willing to admit. Furthermore, these results should give new relevant insights into the influence of stock market index prices on monetary policy in Canada and should provide relevant insights regarding the opportunities and limitations of incorporating financial indicators in monetary policy decision making. They should give financial market participants, such as analysts, bankers and traders, a better understanding of the impact of stock market index prices on the Bank of Canada policy. The results imply that the preferences of the monetary authority have changed between the different subperiods. In particular, the parameter associated with the implicit target of inflation has been reduced significantly. The findings suggest that the introduction of inflation targeting in Canada was accompanied by a fundamental change in the objectives of monetary policy, not only with respect to the average target, but also in terms of precautions taken to keep inflation in check in the face of uncertainty about the economy.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".