Analysis of Symmetric and Asymmetric Effects of Exchange Rate Pass-Through in Inflation-Targeting Countries
Bibliographic record
Abstract
The main purpose of this study is to analyze the effects of symmetry and asymmetry of the exchange rate pass-through in Middle-Income and High-Income countries that implement inflation-targeting policies. This study uses a sample of Middle-Income Countries (South Africa, Brazil, India, Indonesia, and Mexico) and High-Income Countries (Australia, Japan, Canada, Norway, and Sweden) in the form of time-series 2000:Q1- 2021:Q4 with the method of Autoregressive Distribution Lag (ARDL) and Nonlinear Autoregressive Distributed Lag (NARDL). The results showed that five countries have a significant positive effect on the real exchange rate on inflation in the short-run in the ARDL method. In addition, in the NARDL method, five countries significantly positively affect the depreciation of the real exchange rate on inflation in the short-run. Then, only one country has a significant negative effect between the appreciation of the real exchange rate on inflation in the short-run and eight countries in the long-run. Based on the estimation results, it can be concluded that the average quantity of real exchange rate effect on inflation (exchange rate pass-through) in Middle-Income Countries is greater than in High-Income Countries. Therefore, inflation-targeting policies are more flexible to be applied in high-income countries. In addition to the exchange rate, other variables such as oil prices, money supply, and real GDP also greatly affect inflation and have different effects in each country.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".