Optimality, Rational Expectations and Time Inconsistency Applied to Inflation Targeting Strategies
Bibliographic record
Abstract
The purpose of this paper is to analyse the characteristics of an inflation targeting monetary policy, using the Barro-Gordon model specific tools. This paper uses the initial Barro-Gordon concepts of inflationary social costs and benefits, adding a new dimension generated by the cost of output deviating from the potential level. The main contribution of this paper is the exhaustive study of the time inconsistency problem generated by the very existence of a policymaker-established inflation rate. The mathematic simulation of a more complex model than Robert Barro and David Gordon’s model from 1983 allowed a complete analysis of several parameters’ influence (parameters such as the optimal rate of inflation, the discount rate, the importance structure of inflationary social cost) on the appliable range of the target inflation rate, range that guarantees that the policymakers have no incentive to break their own rules, or at least this incentive is somewhat inferior to the future cost of doing so.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".