Exchange Rate Volatility Effect on Economic Growth under Different Exchange Rate Regimes: New Evidence from Emerging Countries Using Panel CS-ARDL Model
Bibliographic record
Abstract
This paper analyzes the impact of exchange rate volatility on economic growth under various exchange rate regimes. It empirically examines this issue in 14 emerging countries from 1990 to 2020. This study has three particularities: First, we use the GARCH model to generate the conditional variance, which will be used as a proxy variable for the exchange rate volatility. Second, to address our issue, we employ the Panel CS-ARDL model, one of the most recent models for handling panel cases. Third, we apply the Dumitrescu and Hurlin Granger non-causality test to capture the potential indirect effect that exchange rate volatility can have on economic growth through the channel of its determinants. The results of our study demonstrate that exchange rate volatility costs emerging countries both directly and indirectly in terms of growth. However, by controlling our countries according to the adopted exchange rate regime, we find that the magnitude of this impact tends to be stifled in the case of countries adopting intermediate exchange rate regimes. Through their combination of rigidity and flexibility, intermediate exchange rate regimes appear to be more effective in mitigating the direct effects of exchange rate volatility on economic growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".