Inflation Persistence and Structural Breaks: The Experience of Inflation Targeting Countries and the US
Bibliographic record
Abstract
This paper reports on a sequential three-stage analysis of inflation persistence using monthly data from 11 inflation targeting (IT) countries and, for comparison, the US, a non IT country with a history of credible monetary policy. First, we estimate inflation persistence in a rolling-window fractional integration setting using the semiparametric estimator suggested by Phillips (2007). Second, we use tests for unknown structural breaks as a means to identify effects of the regime switch and the global financial crisis on inflation persistence. We use the sequences of estimated persistence measures from the first stage as dependent variables in the Bai and Perron (2003) structural break tests. These results suggest that four countries (Canada, Iceland, Mexico, and South Korea) experience a structural break in inflation persistence that coincide with the implementation of the IT regime, and three IT countries (Sweden, Switzerland, and the UK), as well as the US experience a structural break in inflation persistence that coincides with the global financial crisis. Finally, we reapply the Phillips (2007) estimator to the subsamples defined by the breaks. We find that in most cases the estimates of inflation persistence switch from mean-reversion nonstationarity to mean-reversion stationarity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".