A Comparative Analysis of Exchange Rate Pass-Through in China, Eurozone and the U.S.: A Vector Error Correction Model
Bibliographic record
Abstract
This study examines the pass-through effect of fluctuations in the exchange rate on inflation in China in comparison with similar effects in the Eurozone and the United States. Using a set of monthly data covering the period 1999 through 2015 for each case, we constructed a Vector Auto Regressive (VAR) model as well as an Error Correction model (VECM) to estimate the pass-through effects in the three cases. In addition, to ensure that our results are statistically unbiased we also tested the stationarity of the variables of the model. Moreover, to distinguish between the short-run and long-run pass-through effects, we made use of a series of co-integration tests. Our results indicate that the pass-through effect of changes in the exchange rate in China is much weaker than it is in the Eurozone and the United States. We found this effect in the U.S. to be both more notable and longer-lasting.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".