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Record W7143816192 · doi:10.15057/30822

石油価格変動が為替レートとマクロ変数に与える影響 : A Multi-Country Analysis

2019· article· ja· W7143816192 on OpenAlexaboutno aff
Tokuo Iwaisako, Hayato Nakata

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2019
Typearticle
Languageja
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsExchange rateVector autoregressionStructural vector autoregressionOil priceStructural breakMacroSample (material)Demand shock

Abstract

fetched live from OpenAlex

This paper employed a structural vector autoregression model in a quantitative assessment of the effect of exogenous shocks related to oil price determination on countriesʼ exchange rates and outputs. Because we were interested in the effect of oil price changes on energyexporters and importers, we chose Australia, Canada, Japan, Norway, and the UK as sample countries. This model comprised of four structural shocks :(i)oil supplyshocks,(ii)global demand shocks,(iii)oil price fluctuations that are not related to supplyand demand, and(iv)pure exchange rate fluctuations that are not related to other structural shocks. Various responses to structural shocks explain the correlation structure of the currencies. Moreover, pure exchange rate shocks are the main sources of exchange rate volatility.We also examined the role of structural shocks in explaining macro variables, taking Australia and Japan as examples. We thus discovered that global demand shocks and non-fundamental oil price fluctuations have a strong impact on GDP and export growth for both countries, while pure exchange rate shocks were relativelyunimportant in explaining Japanʼs macroeconomic variables.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.237
Teacher spread0.218 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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