Symmetric or Asymmetric: How do Sri Lanka’s Bilateral Trade Balances Respond to Real Exchange Rate Changes?
Bibliographic record
Abstract
This paper examines the impact of real exchange rate fluctuations on the trade balance between Sri Lanka and its major trading partners using the Non-linear Auto Regressive Distributed Lag model. Analysing data from the first quarter of 2007 to the fourth quarter of 2022, covering 10 trading partners, the study reveals varied reactions in the trade balance to real exchange rate depreciation versus appreciation, supporting an asymmetric effect. While some results align with conventional exchange rate theory, showing a positive long run effect of real depreciation, others indicate only a short run effect. The mixed empirical findings underscore the lack of uniformity in the relationship between real exchange rates and trade balances, which can be attributed to differences in the composition of traded goods. Given these asymmetries, it is evident that exchange rate policy alone has limited capacity to enhance the overall trade balance of the country. Therefore, adopting a comprehensive policy package to ensure Sri Lanka’s global competitiveness is imperative to effectively address the multifaceted nature of trade dynamics.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".