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Record W4388098847 · doi:10.1080/00036846.2023.2273243

World uncertainty and commodity currencies

2023· article· en· W4388098847 on OpenAlexaboutno aff
Joseph Agyapong

Bibliographic record

VenueApplied Economics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsLiberian dollarExchange rateShock (circulatory)Monetary economicsCurrencyCommodityInternational economicsU.S. Dollar IndexCommodity marketUs dollarFinance

Abstract

fetched live from OpenAlex

This paper contributes to the literature by analysing shock propagation mechanisms between world uncertainty, exchange rates and country-specific commodity terms of trade. Using monthly data from 2008 to 2020 for eight commodity currencies’ exchange rates, we analyse the impulse responses based on local projections. The study results show that a shock from the exchange rates which are net transmitters of shocks through the direct United States (US) dollar effect causes world uncertainty to rise and subsequently fall due to the indirect commodity terms of trade effect. Also, in response to world uncertainty shock, the exchange rates fall in the risk-on period and subsequently overshoot during the risk-off period when investors seek the safe haven of the dollar. The world uncertainty shock on the exchange rate is predominantly experienced in the economies that largely trade commodities with the US, particularly Russia and Canada. The study finds evidence that the dollar is a prime cause of world uncertainty. Hence, for policy implications, the study discusses that policymakers, investors, or traders pay attention to the dollar which is the main invoicing currency in the international market.

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)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.819

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)0.0000.000

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 head0.037
GPT teacher head0.219
Teacher spread0.182 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations4
Published2023
Admission routes1
Has abstractyes

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