Estimating the Impact of China’s Central Bank Intervention on the RMB/US$ Exchange Rate Misalignment
Bibliographic record
Abstract
The paper studies the misalignment path of the RMB/US$ exchange rate, focusing on the managed floating period since July 2005 and determining the impact of the central bank’s intervention on the RMB/US$ misalignment. We adopt the permanent-and-transitory component decomposition approach developed by Gonzalo and Granger (1995) to estimate the equilibrium RMB/US$ rate and its misalignment within a vector error correction model (VECM). The sample for our study is the quarterly data on the exchange rate and some fundamental variables for the US and China between the 1st quarter of 2000 and the 2nd quarter of 2020. The results show a trend of the RMB/US$ reducing its undervaluation during the sample period, going from 41% to 35%. The government intervention substantially increased the RMB undervaluation, from an average of 5% to 39% without accounting for the weak exogeneity of the intervention, but to 25.8% after accounting for the exogeneity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".