Effects of Real Exchange Rate Volatility on Trade: Empirical Analysis of the United States Exports to BRICS
Bibliographic record
Abstract
This paper analyzes the effects of real exchange rate volatility on the United States’ exports to BRICS. It focuses on the top 20 export products (defined by the 2-digit Harmonized System codes) from the United States to Brazil, Russia, India, China, and South Africa, and uses quarterly data for period from 1993Q1 to 2021Q2. The specified panel regression model was first estimated using three estimation methods, namely, the Panel Least Squares, the Panel Fully Modified Least Squares (FMOLS), and Panel Dynamic Least Squares (DOLS). In addition, to estimate the short-run and long-run effects of real exchange rate volatility on exports, it also uses the method of the Autoregressive Distributed Lag (ARDL) approach to cointegration analysis and error-correction models. Two measures of exchange rate volatility are used in this study. According to our findings, the levels of foreign economic activity have a positive effect on exports while the real exchange rate has a negative effect on exports. In addition, exchange rate volatility has a negative effect on exports in the long run in all five countries. However, the effects of exchange volatility are found to yield mixed results in the short run regardless of which measure of exchange rate volatility was used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".