Identifying a policymaker’s target: an application to the Bank of Canada
Bibliographic record
Abstract
We develop a new way to test hypotheses about policymakers’ targets and implement that test for Canadian monetary policy. For example, if the Bank of Canada is targeting a 2 per cent inflation rate, and if the Bank’s instrument takes eight quarters to affect inflation, then deviations of inflation from 2 per cent should be uncorrelated with the Bank’s information set lagged eight quarters. We show that there was a major change in the Bank’s objectives near the time when formal inflation targets were announced and that the Bank has indeed been targeting inflation since then. JEL Code: E52, E61 Identifier une cible du définisseur de politique: une application à la Banque du Canada. Les auteurs développent une nouvelle manière de tester des hypothèses quant aux cibles des définisseurs de politiques, et utilisent ce protocole pour analyser la politique monétaire canadienne. Par exemple, si la Banque du Canada s’est donnée pour cible un taux d’inflation de 2 pour‐cent, et si l’instrument utilisé par la Banque du Canada met huit trimestres à avoir son effet sur le taux d’inflation, alors les déviations de l’inflation autour de 2 pour‐cent ne devraient pas être co‐reliées à l’information disponible aux autorités monétaires quand elle a agi. On montre que il y a eu changement dans les objectifs de la Banque aux environs du moment où les cibles formelles d’inflation ont été annoncées, et que la Banque a de fait ciblé le taux d’inflation depuis.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".