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Record W2497382441 · doi:10.1002/asmb.2189

Testing unit roots, structural breaks and linearity in the inflation rates of the G7 countries with fractional dependence techniques

2016· article· en· W2497382441 on OpenAlexaffabout
Luis A. Gil‐Alana, OlaOluwa S. Yaya, Enitan A. Solademi

Bibliographic record

VenueApplied Stochastic Models in Business and Industry · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSeries (stratigraphy)Unit rootEconometricsInflation (cosmology)MathematicsMean reversionLinearityEconomicsLong memoryStructural breakChebyshev polynomialsStatisticsApplied mathematicsMathematical analysisPhysicsGeology

Abstract

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In this paper, we have examined the inflation rates in the Group of Seven countries, investigating issues such as the existence of unit roots, structural breaks, fractional integration and potential non‐linearities using a fractional dependence (FD) approach based on Chebyshev polynomials in time. This robust FD approach allows one to test for persistence as well as non‐linearity of the series. We first tested for stationarity and structural breaks using classical approaches and observed inconclusive results with regard to the stationarity levels of the series. Using Bai–Perron tests, we actually confirmed significant structural breaks, even up to five, in each of the inflation series. However, noting that structural breaks are significantly related to fractional differentiation, this latter approach was also conducted. Here, we observed that the estimates of the differencing parameter were quite stable across time, and evidence of unit roots was found in the cases of the UK, Canada, France, Japan and the USA; for Germany, we found some evidence of mean reversion, while estimates of d above 1 were found in the case of Italy. On the other hand, non‐linear deterministic trends were clearly rejected in all cases. Copyright © 2016 John Wiley & Sons, Ltd.

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.

How this classification was reachedexpand

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)0.000
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 score0.207
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.073
GPT teacher head0.243
Teacher spread0.170 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2016
Admission routes2
Has abstractyes

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