Uncovering The Hit-List For Small Inflation Targeters: A Bayesian Structural Analysis
Bibliographic record
Abstract
We estimate underlying structural macroeconomic policy objectives of three of the earliest explicit inflation targeters within the context of a small open economy dynamic stochastic general equilibrium model. We assume central banks set policy optimally, such that we can reverse engineer policy objectives from observed time series data. Joint tests of the posterior distributions of these policy preference parameters suggest that the central banks are very similar in their overall objective. None of the central banks show a concern for stabilizing the real exchange rate. All three central banks share a concern for minimizing the volatility in the change in the nominal interest rate. We also show that the resulting optimal policy rule responds to exchange rate movements, even in the case where the central banks do not explicitly care about exchange rate stabilization. This result is also corroborated by results from an alternative simple-rule characterization and estimation of central bank behavior. These last two findings point to the pitfalls of making inferences, from the level of "ad hoc" simple rules, about what central banks may care about. Copyright (c) 2009 The Ohio State University.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".