CYCLICAL BEHAVIOR OF PRICES IN THE G7 COUNTRIES THROUGH WAVELET ANALYSIS
Bibliographic record
Abstract
Our analysis, conducted using the GDP and the GDP deflator time series (OECD source; 1960–2001) for the G7 countries, shows the robustness of the negative covariance between the GDP and its deflator, but only over long run horizons. Through wavelet decomposition we evaluate the price–output relationship at different time scales, where most countries reveal similar patterns. More precisely, at short time scales a positive correlation seems to appear whereas, and consequently, a regime switch occurs at a time horizon of about two years leading to a negative relationship for higher horizons. These results seem to suggest that the negative or acyclical relationship usually found after the 1960s may be the composite effect of different time scale correlations, where the four-year-horizon component seems to have the greatest influence. In particular for Canada, France, and Italy we observe something like a rotation of the price–output relationship between the countercyclical and the procyclical relationship. Finally, our analysis shows that even the relationship between the two series does not seem to be very stable regarding the lead and lag structure also. The phase is nonlinear for all the countries and, consequently, the group delay (the lag) is not constant. In particular, looking at the time scale we observe an inversion of the local monotonicity at the frequency of about 0.3–0.35 for all G7 countries.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".