Comments by Arthur Grimes Comments on: “The implications of uncertainty for monetary policy”
Bibliographic record
Abstract
As a former monetary policymaker, I can testify, with certainty, to the importance of the subject matter of Shuetrim and Thompson’s paper. Policymakers are faced with all sorts of uncertainties. The paper comes to interesting, and somewhat surprising, conclusions. I am uncertain, however, the extent to which the conclusions are specific to the author’s approach, as opposed to having more general validity. In this commentary, I summarise briefly what I see as the major contribution of the paper. I then analyse various features which may cause us to question the generality of the paper’s results. However, these features do not deny the possibility that the paper’s findings are correct. I discuss the possibility that if one accepts the findings are correct, they imply that policy targets, rather than policy implementation, may need attention. Key findings Monetary policymakers are normally characterised as conservative gentlemen 1 who implement conservative policies. This includes the practice of not implementing large changes in policy instruments (vis the prevalence over the past decade in the United States and elsewhere of quarter to half percentage point movements in discount rates at times of policy changes).
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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 | 0.013 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.033 | 0.046 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".