MétaCan
Menu
Back to cohort
Record W1978325078 · doi:10.1142/s0219525908001520

CYCLICAL BEHAVIOR OF PRICES IN THE G7 COUNTRIES THROUGH WAVELET ANALYSIS

2008· article· en· W1978325078 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAdvances in Complex Systems · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsGDP deflatorEconomicsEconometricsLagCovarianceVolatility (finance)MathematicsStatisticsReal gross domestic product

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.287
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it