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Record W2095880798 · doi:10.5539/ijef.v7n10p91

Fiscal Expenditure, Pricing-to-Market and Exchange Rate Behavior

2015· article· en· W2095880798 on OpenAlexvenueno aff
Chung-Fu Lai, Drow-Tai Chen

Bibliographic record

VenueInternational Journal of Economics and Finance · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsExchange rateMonetary economicsGovernment expenditureShock (circulatory)CurrencyCurrency substitutionExchange-rate pass-throughElasticity of substitutionFiscal policyMacroeconomicsDevaluationPublic financeProduction (economics)

Abstract

fetched live from OpenAlex

This paper investigates the effects of fiscal expenditure shock on exchange rate behavior and the role of asymmetric pricing-to-market in the New Open Economy Macroeconomics. The findings of this paper indicated that if discriminatory pricing behavior is considered, and when a country faced with a fiscal expenditure shock, exchange rate fluctuation in the short run would be wider than in the long run with overshooting of exchange rate. Further, the increase of government expenditure will push up exchange rates. If the firms in both countries take pricing based on home (foreign) currency, an enlargement of the size of the home country will cause lesser (wider) range of exchange rate fluctuation with the change in government expenditure. In addition, the greater the effect of the elasticity of substitution among the products and marginal utility of the real money demand will trigger lesser range of exchange rate fluctuation with the change in government expenditure.

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)0.005
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.075
GPT teacher head0.257
Teacher spread0.181 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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