Nonlinear ARDL approach, asymmetric effects and the J-curve
Bibliographic record
Abstract
Purpose – Previous research that investigated the effects of currency depreciation on the trade balance assumed that the adjustment of all variables in a given model is in linear fashion. The authors wonder if introduction of nonlinearity in the adjustment of some variables such as the exchange rate can shed additional light on evidence of the J-curve. The new approach also allows to test whether exchange rate changes have symmetric or asymmetric effects on the trade balance. Estimates of a trade balance model for Canada, China, Japan, and the USA reveal that the effects are indeed asymmetric. The paper aims to discuss these issues. Design/methodology/approach – The methodology is based on linear and nonlinear ARDL approach. Findings – When nonlinearity is introduced into testing approach for the J-curve, more evidence is found in support of the J-curve. Research limitations/implications – The models are estimated using aggregate trade flows of each country with the rest of the world, hence they suffer from aggregation bias. Using trade flows at bilateral level and at commodity level are highly recommended for future research. Originality/value – This is the first paper that applies nonlinear ARDL approach to test the short-run and long-run effects of currency depreciation on the trade balance.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".