Contrasting Quantitative and Qualitative Assessments of Central Bank Behavior and the Evolution of Monetary Policies
Bibliographic record
Abstract
INTRODUCTION It is apparent from the results so far that there is a gap between the qualitative and econometric evidence. The former predicts not only that central banks can be fairly easily classified according to the degree of statutory autonomy enjoyed vis-à-vis government but that there is also a clear empirical connection between their independence vis-à-vis government and average inflation performance. By contrast, the econometric evidence would lead one to conclude that central banks in the industrial world are not as different as the qualitative evidence implies. This chapter attempts to provide explanations for the conflicting evidence. It is argued that certain elements in the measurement of central bank behavior are difficult to quantify, are imperfectly measured, or have evolved over time in a manner that is not easily reconciled by the two approaches. This possibility was already discussed in Chapter 2. Moreover, while the exchange rate regime clearly matters, it appears to matter less than would be suggested by the attempts to refine existing classifications in relation to textbook descriptions. Of course, as we have seen in Chapter 2, dating a change in policy regimes is a tricky matter. Conflict between official dates and ones estimated via econometric methods rest in part on how fast individuals' expectations respond to actual changes in the variables of interest. In addition, until recently, exchange rate regime considerations rarely figured as an explicit feature of the statutory relationship between the central bank and governments.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".