The Relationship between RMB Exchange Rate and Chinese Trade Balance: Evidence from a Bootstrap Rolling Window Approach
Bibliographic record
Abstract
This study inspects the fundamental relationship between the exchange rate and the trade balance in China. The outcome shows that the real effective exchange rate and trade balance in China has no causal relationship. Though, seeing structural changes in two series, we got the result those both long-run and short-run associations using full-sample data are wobbly, which proposes that the full-sample causation tests can’t be relied upon. Then, using time-varying rolling window method to reexamine the dynamic fundamental relationship. The results show that real effective exchange rate has both negative and positive impacts on the trade balance in several sub-periods, and in turn, trade balance has same impact on real effective exchange rate for China. These findings provide no support for the existence of J-curve effect and Marshall-Lerner Condition in case of China. This study shows that it is impossible to resolve China’s trade deficit, depending only on the movement of RMB’s exchange rate.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".