Bibliographic record
Abstract
In March 2001 Iceland introduced inflation targeting. In the three years that followed, inflation was quickly stabilized at the target rate and fluctuated well within the Central Bank's tolerance band. However, since February 2005 inflation has often been above the upper tolerance limit. This raises the question of how tightly is it feasible to control inflation in a very small open economy like Iceland. This paper attempts to provide some empirical answers to this question using small estimated macroeconomic models of Iceland, New Zealand, Canada, the United Kingdom and the United States. These models are used to derive efficient monetary policy frontiers that trace of the locus of the lowest combinations of inflation and output variability that are achievable under a range of alternative rules for operating monetary policy. These efficient policy frontiers illustrate that inflation stabilization is a considerably more daunting challenge in Iceland than in other industrial countries, even other very small industrial countries like New Zealand. The key reason for this result is the relative magnitudes of the shocks to which the economy is subjected. If inflation outside the target band undermines the credibility of monetary policy, thereby increasing the real cost of maintaining price stability, these results suggest that the inflation targeting framework will need to continue to evolve to reduce the probability that targeted inflation will breach the tolerance range. Further, other macroeconomic policy changes, such more systematic coordination between monetary and fiscal policy, should be considered to help further reduce inflation and output variability in Iceland.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".