Inflation Targeting: It's Not Broke, it Doesn't Need Fixing, But Can it Survive?
Bibliographic record
Abstract
Until the end of 2005 there were few outward signs that the inflation targeting (IT) monetary policy strategy was deemed fragile or that the likelihood of abandoning it was high. In light of the severe economic downturn and the global financial crisis that has afflicted most economies around the world since at least 2008, it is worth reconsidering the question of the fragility of the inflation targeting regime. This paper reprises the approach followed in Siklos (2008) but adds important new twists. For example, the present study asks whether the continued survival of IT is due to the fact that some of the central banks in question did take account of changes in financial stress. The answer is no. Indeed, many central banks are seen as enablers of rapid asset price increases. The lesson, however, is not that inflation targeting needs to be repaired. Instead, refinements should be considered to the existing inflation targeting strategy which has evolved considerably since it was first introduced in New Zealand 20 years ago. Most notably, there should be continued emphasis on inflation as the primary nominal anchor of monetary policy, especially in emerging market economies (EME), even if additional duties are assigned to central banks in response to recent events.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".