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Record W261822845 · doi:10.1017/s1365100516000249

OIL, UNCERTAINTY, AND GASOLINE PRICES

2016· article· en· W261822845 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMacroeconomic Dynamics · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGasolineEconomicsEconometricsCrude oilVector autoregressionAutoregressive conditional heteroskedasticityOil priceWest Texas IntermediateMultivariate statisticsPetroleumStructural vector autoregressionFinancial economicsVolatility (finance)Monetary economicsStatisticsMathematicsPetroleum engineeringChemistryEngineeringMonetary policyWaste management

Abstract

fetched live from OpenAlex

In this paper we investigate the relationship between crude oil and gasoline prices and also examine the effect of oil price uncertainty on gasoline prices. The empirical model is based on a structural vector autoregression that is modified to accommodate multivariate GARCH-in-Mean errors. We use monthly data for the United States over the period from January 1976 to September 2014. We find that there is an asymmetric relationship between crude oil and gasoline prices, and that oil price uncertainty has a positive effect on gasoline price changes. Our results are robust to alternative model specifications and alternative measures of the price of oil.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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