Revisiting China’s Exchange Rate Regime and RMB Basket: A Recent Empirical Study
Bibliographic record
Abstract
China announced to adopt managed floating exchange rate regime with reference to a basket of currencies in July 2005, but did not unveil exact weight of each of component currencies in RMB basket. We attempt to find the latest evidence on how the RMB pegs to its currency basket. This research begins with a short review of the evolutionary history of the RMB exchange rate regime in different periods from 1949 to 2013. We then move forward to examine how the RMB pegs to its currency basket and estimate the weight of each of component currencies over the period from January 2007 to March 2013 by applying Frankel and Wei (1994)’s model. The results illustrate that the RMB did not perfectly peg to RMB basket as the PBoC announced during the period from January 2007 to March 2013. But we find the US dollar’s weight has declined steadily since 2011, although it still is the most important reference currency to peg for the RMB. Interestingly, other currencies like Singapore dollar received increasing weight in RMB basket. It implies that RMB exchange rate regime is in the transitional period from single currency peg to currency basket peg.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".