Bibliographic record
Abstract
The relationship between real effective exchange rate (REER) of RMB from 1997 to 2006 and trade surplus between China and other trade partners was discussed using cointegration vector autoregression in the paper. The study shows that there exists a long-standing and stable relationship between REER of RMB and trade balance; the fall of the real effective exchange rate of RMB is one of the reasons of the increasing trade surplus, however, the influences it brings are less than domestic GDP and trade partner’s GDP do. So, increasing the flexibility of the exchange rate of RMB, broadening the exchange rate’s float space between RMB and dollar are a necessary part of the package policies in solving the trade surplus. Key words: RMB appreciation, real effective exchange rate, trade surplus, cointegration vector autoregression Resume: La relation entre le taux de change reel (TCR) de RMB de 1997 a 2006 et le surplus commercial de la Chine avec les autres partenaires commerciaux est discutee, en utilisant l’autoregression de vecteur de cointegration, dans l’article present. L’etude montre qu’il existe un lien stable de longue date entre TCR de RMB et la balance commerciale, et que la baisse de TCR de RMB est une des raisons du surplus commercial croissant. Neanmoins, ses influences sont moins importantes que celles de PIB de notre pays et des partenaires. Ainsi, renforcer la flexibilite du taux de change de RMB, elargir l’espace de flottement du taux de change entre RMB et le dollar sont necessaire pour resoudre le probleme de surplus commercial. Mots-Cles: appreciation de RMB, taux de change reel, surplus commercial, autoregression de vecteur de cointegration
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".