A Survey of Taylor-Type Monetary Policy Rules,”Canadian Department of Finance Working Paper
Bibliographic record
Abstract
Working Papers are circulated in the language of preparation only, to make analytical work undertaken by the staff of the Department of Finance available to a wider readership. The paper reflects the views of the authors and no responsibility for them should be attributed to the Department of Finance. Comments on the working papers are invited and may be sent to the author(s). Les Documents de travail sont distribués uniquement dans la langue dans laquelle ils ont été rédigés, afin de rendre le travail d’analyse entrepris par le personnel du Ministère des Finances accessible à un lectorat plus vaste. Les opinions qui sont exprimées sont celles des auteurs et n’engagent pas le Ministère des Finances. Nous vous invitons à commenter les documents de travail et à faire parvenir vos commentaires aux auteurs. There are numerous ways to specify monetary-policy reaction functions in economic models. In recent years, the practice of using the Taylor rule, or more generally, Taylor-type rules, as a monetary policy rule has become more wide spread in the economic literature. The structure of this rule is simple and tractable but at the same time captures the essence of the behaviour of the monetary authority. In this paper, we present a brief survey of the literature on the properties of Taylor-type monetary policy rules. We review the modifications made to the
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".