A Comparison of Optimal Policy Rules for Pre and Post Inflation Targeting Eras: Empirical Evidence from Bank of Canada
Bibliographic record
Abstract
In this paper, we derive policy rules of Bank of Canada for different preferences over the goal variables in their loss functions. The optimal rules are derived for the pre and post inflation-targeting eras. According to the results, the monetary policy rule of the Bank of Canada for the pre inflation-targeting era is best described with a loss function that attaches equal weight to inflation, interest rate smoothing incentive and the output gap in the loss function. In the post-inflation targeting era the optimal interest rate attaches the highest weight to inflation rate in the loss function; followed by the interest rate smoothing incentive and then the output gap. The inclusion of the exchange rate as another goal variable in the loss function does not significantly alter the results in approximating the actual policy rate of Bank of Canada. Next, simulations of demand (positive) and supply (negative) shocks are carried out for the post-IT period for two cases where the monetary policy rule is mimicked by (i) an ad-hoc Taylor rule and (ii) the derived optimal rule. The results indicate that the ad-hoc Taylor rule brings down inflation rates more quickly compared to the derived optimal rule, but only at the cost of higher contraction in output and more volatile interest rates.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".