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Exchange Rate, Trade Balance, and the J-Curve Effect in Vietnam

2017· article· en· W2751911578 on OpenAlexaboutno aff
Thi Xuan Thom

Bibliographic record

VenueAsian Economic and Financial Review · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsDevaluationBalance of tradeExchange rateEconomicsQuarter (Canadian coin)Balance (ability)EconometricsInternational economicsMonetary economicsGeographyPsychology

Abstract

fetched live from OpenAlex

The aim of this study is to analyse movements of exchange rate and trade balance during 2001-2015 and investigate whether a devaluation would improve the trade balance by a J-curve effect. The study uses quarterly data collected from reliable sources such as World Bank (WB), and General Statistic Office of Vietnam (GSO), and International Financial Statistics (IFS). The first part of the study shows the performance of the trade balance and exchange rate movements during the given period. The second part employs Impulse Response analysis to examine the pattern of the trade balance after a shock of exchange rate or a devaluation. The finding of the study is that following a devaluation, the trade balance deteriorates in the first two-quarters and then starting improving till the sixth quarter. After the six quarter, the trade balance again falls into deficit and followed by rises and declines unexpectedly. With responses illustrated from the analysis, the trade balance has a sign of the J curve in early quarters but this sign is fading in later quarters. The study also discovers that there is a possibility that after a shock of exchange rate, the trade balance will follow an S curve, instead of a J curve. In the end of the study, the author recommends some policy implications to improve trade balance and open further insights for subsequent researchers.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.747
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.234
Teacher spread0.216 · 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 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

Citations4
Published2017
Admission routes1
Has abstractyes

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