A Nonparametric Approach to Evaluating Inflation-Targeting Regimes
Bibliographic record
Abstract
We use a variety of nonparametric test statistics to evaluate the inflation- targeting regimes of Australia, Canada, New Zealand, Sweden and the UK. We argue that a sensible approach of evaluation must rely on a variety of methods, among them parametric and nonparametric econometric methods, for robustness and completeness. Our evaluation strategy is based on examining two possible policy implications of inflation targeting: First, a welfare implication and second, a real variability implication. The welfare implication involves evaluating a utility function, and tested by testing whether (1) the distributions of the levels and the growth rates of private consumption and leisure per capita remained unchanged under inflation targeting, i.e., first-order stochastic dominance; and (2) testing a linear combination of consumption and leisure per capita, where the parameter describing the utility of leisure or the relative preference of leisure is calibrated. Then we introduce nonparametric univariate and multivariate statistical methods to test whether the first and second moments of a variety of real variables, such as the real exchange rate depreciation rate, real GDP per capita growth rate in addition to private consumption per capita and leisure per capita growth rates, remained unchanged under inflation targeting, decreased or increased significantly. There seems to be some evidence of increased welfare under inflation-targeting regimes, but no concrete evidence is found that inflation targeting policy, in general, reduces real variability. Some cross country differences are also found.
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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 | 0.018 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".