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Enregistrement W832564660

How Oil-Price Shocks Affect Producers and Consumers

2014· article· en· W832564660 sur OpenAlexaboutno aff
Ryan Kellogg

Notice bibliographique

RevueEconstor (Econstor) · 2014
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueMarket Dynamics and Volatility
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBrent CrudeOil-storage tradeOil priceEconomicsBarrel (horology)West Texas IntermediateCrude oilVolatility (finance)RecessionPetroleumCrack spreadMonetary economicsFinancial economicsKeynesian economicsGeologyPetroleum engineeringEngineeringPaleontology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Markets for crude oil have been characterized by multiple episodes of volatility over the past 20 years. The price of Brent crude oil, an international crude oil benchmark priced in the North Sea, varied from a low of about $10 per barrel (bbl) in 1999 to a peak of more than $ 140/bbl in 2008, before falling again during the Great Recession. While the Brent price stabilized around $110/bbl during 2010-13, it recently and suddenly collapsed to around $50/bbl. The majority of these oil price swings have been attributed to global demand shocks such as the Great Recession, though the price drop this past autumn has not yet been extensively studied. (1) The accompanying figure shows the price both of Brent light and West Texas Intermediate (WTI) crude oil, which is priced in Cushing, Oklahoma. Historically, the WTI and Brent crude oil prices tracked each other extremely closely. However, beginning in 2011 these two price series diverged substantially, with WTI sometimes falling more than $20/bbl below Brent. This gap has recently closed substantially, but not entirely. In a series of papers, my co-authors and I have studied how shocks to crude oil markets affect oil producers and consumers. We have addressed questions such as How do oil drilling and production respond to oil price shocks?, Is oil price volatility itself important?, and How do consumers forecast future price changes? This research summary briefly describes these papers and notes issues where future research is needed. The Cushing Oil Glut In a recent project, Severin Borenstein and I studied the divergence between WTI and Brent oil prices that began in 2011. (2) This divergence arose from the confluence of a dramatic increase in unconventional crude oil production in Alberta, North Dakota, and West Texas and a lack of sufficient pipeline capacity to transport this new crude oil to Gulf Coast refineries. These factors led to a of oil at Cushing, Oklahoma, depressing the WTI price relative to the price of international crude oil. Our paper focuses on whether this decrease in the WTI price passed through to regional gasoline and diesel prices. [GRAPHIC OMITTED] Using data from the Energy Information Administration (EIA) on wholesale refined product markets, we find that gasoline and diesel prices in the Midwest, including Oklahoma, did not decrease at all in response to the glut of crude oil at Cushing. This lack of response is explained by the fact that, even though crude oil pipeline capacity was constrained after 2011, refined-product pipeline capacity was not. Thus, the marginal barrels of gasoline and diesel in the Midwest were, and still are, imported from the Gulf Coast, where they are refined using high-cost internationally-procured crude oil. These results imply that increases in crude oil production in the central U.S. did not lead to benefits for local consumers in the form of lower gasoline prices. Instead, Midwest refiners profited from the large spread between Midwest prices for crude oil and refined products. Since the publication of our paper, a series of significant pipeline investments has substantially decreased the spread between WTI and Brent oil prices. As our paper predicted, the relative increase in the WTI price has not passed through to Midwest refined product prices. Still, the WTI-Brent price wedge has not completely closed, owing to the U.S. ban on crude oil exports and to the fact that shale oil from North Dakota and West Texas is relative to imported crude. Because most U.S. Gulf Coast refineries are designed to handle heavy crude oil, they only purchase light crude oil at a discount, creating a differential relative to the international price. This situation presents a clear need for research into the economics of lifting the U.S. crude oil export ban, including a detailed analysis of how changes in light vs. heavy crude oil use by refineries would affect U. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,019

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0020,002
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,204
Écart entre enseignants0,190 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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