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Record W2743604298 · doi:10.5539/ijef.v9n9p52

What Affects the Relationships between Oil and Industrial Sector? Case of Eurozone

2017· article· en· W2743604298 on OpenAlexvenueno aff
Melik Kamışlı, Serap Kamışlı, Fatih Temi̇zel, Ethem ESEN

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
FundersAnadolu Üniversitesi
KeywordsCointegrationEconomicsEnergy sectorProfitability indexMonetary economicsInvestment (military)DebtPortfolioOil priceSecondary sector of the economyIndustrial productionMacroeconomicsEconomyFinancial economicsFinanceEconometricsEconomic system

Abstract

fetched live from OpenAlex

Oil, which is one of the fundamental energy sources, is an important cost item especially for industrial sector. Increases in oil prices decrease the profits of the firms by causing increase in the production costs. For this reason, it is claimed that there is a strong relationship between oil price and industrial sector profitability. On the other hand, oil is an alternative investment vehicle that can be included to the portfolio. Therefore, in this study the relationships between oil price and industrial sector returns of European countries are analyzed with Maki (2012) cointegration test under multiple structural breaks, on the basis of European Debt Crisis. The results show that announcements of credit rating agencies, elections, resignations, announcements of European Central Bank and IMF, recovery packages and economic developments cause structural breaks in relationships. Results also indicate that there is no cointegration between oil price and industrial sector returns of Austria, Belgium and Holland.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.089
GPT teacher head0.261
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2017
Admission routes1
Has abstractyes

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