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The role of higher oil prices: A case of major developed countries

2009· book-chapter· en· W167234723 on OpenAlexaboutno aff
Terence J. O’Neill, Jack H.W. Penm, R. Deane Terrell

Bibliographic record

VenueResearch in finance · 2009
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsStock (firearms)Monetary economicsInflation (cosmology)Stock marketOil priceEmerging marketsMacroeconomicsGeography

Abstract

fetched live from OpenAlex

The primary aim of this chapter is to examine whether the recent increase in world oil prices has affected inflation expectations and stock market returns in major OECD countries. The key findings are as follows. First, we found no evidence to support the presence of a long term relationship between oil prices and inflation expectations – measured by the difference between yields of inflation indexed and non-inflation indexed government bonds – over the sample between early 2003 and late 2006. Second, higher oil prices are found to lead to expectations of higher inflation. This evidence is stronger over the period where oil prices had been higher and signs of capacity constraints in the economy were emerging. Third, the impact of higher oil prices on stock market returns differs among countries. While higher oil prices are found to adversely affect stock market returns in the United States, the United Kingdom and France, the effects are positive in Canada and Australia as these countries are significant exporters of energy resources.

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 score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.306
Teacher spread0.238 · 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

Citations109
Published2009
Admission routes1
Has abstractyes

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