Identifying Changes in Mean, Seasonality, Persistence and Volatility for G7 and Euro Area Inflation
Bibliographic record
Abstract
This study examines the properties of monthly CPI inflation in G7 countries and the Euro area (aggregate) over the period 1973-2007 using a new iterative decomposition procedure that separates changes in mean, seasonal and dynamic components together with conditional volatility. We uncover mean and seasonality breaks for all countries and, even allowing for these, changes in persistence are indicated for all countries except Canada. Further, while volatility reductions are widespread in the mid- to early 1980s, Canada, France and the US all exhibit increased volatility from 1999 onwards. Of methodological interest, iteration is shown to provide more evidence of persistence breaks and fewer volatility breaks overall compared with the usual approach of sequentially examining changes in the properties of inflation, while application of linear seasonal adjustment also reduces evidence of persistence breaks. Although failure to allow for breaks in mean, seasonal or dynamic components affects conclusions about the existence and dates of volatility breaks, nevertheless, evidence remains of a volatility increase in some countries in 1999.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".