Adopting Price-Level Targeting under Imperfect Credibility
Bibliographic record
Abstract
Authors' note: After completing this working paper, we realized a computer coding error, which led to an overstatement of transition costs due to imperfect credibility. The corrected results show that it takes at least 10 quarters of low credibility (as opposed to 2 quarters in the old version) for a policy change from inflation targeting to price-level targeting to be welfare reducing. The revised version of this paper with corrected transition costs is forthcoming as a Bank of Canada Working Paper. This paper measures the welfare gains of switching from inflation-targeting to price-level targeting under imperfect credibility. Vestin (2006) shows that when the monetary authority cannot commit to future policy, price-level targeting yields higher welfare than inflation targeting. We revisit this issue by introducing imperfect credibility, which is modeled as gradual adjustment of the private sector's beliefs about the policy change. We find that gains from switching to pricelevel targeting, if any, are small.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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; both teacher heads agree on what is shown here.
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".