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Record W4281928167 · doi:10.5539/ijef.v14n6p62

Exchange Rate and Trade Balances in Brazil: A Disaggregated Analysis by Major Economic Categories

2022· article· en· W4281928167 on OpenAlexvenueno aff
Elano Ferreira Arruda, Antônio Clécio de Brito, Pablo Urano de Carvalho Castelar

Bibliographic record

VenueInternational Journal of Economics and Finance · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsBalance of tradeCapital goodExchange rateDurable goodCapital (architecture)Balance (ability)Work (physics)Monetary economicsInternational economicsMacroeconomicsGoods and servicesEconomy

Abstract

fetched live from OpenAlex

This work investigates the repercussions of real devaluations in the exchange rate on the trade balance for Brazil, when considering major economic categories, i.e., capital goods, durable consumer goods, semi-durable and non-durable consumer goods, intermediate goods, and fuels and lubricants. To this end, monthly data are used for the period January 2000 and July 2019, and vector error correction (VEC) models. The results suggest that, in the long run, real devaluations in the exchange rate have positive and elastic impacts on the trade balance in all sectors, except for fuels and lubricants. Only the durable consumer goods and fuels and lubricants sectors do not show the occurrence of the J curve. Domestic income has a negative impact on the trade balance in most models analyzed, while foreign income has a positive impact on all sectors, except for fuels and lubricants.

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 score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.021
GPT teacher head0.227
Teacher spread0.205 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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