Is there J‐curve effect in the US Service Trade? Evidence from asymmetric analysis
Bibliographic record
Abstract
Abstract The J‐curve hypothesis asserts that a depreciation could worsen the trade balance in the short run but improves it in the long run. In testing the hypothesis, almost all previous studies used trade data in goods only. We add to this literature by considering the US trade in insurance and financial services with each of its nine trading partners. Using quarterly data over the period 2003Q1–2019Q4, when we estimated a linear model, we found limited support for the J‐curve effect. However, when we estimated a nonlinear model to assess the possibility of asymmetric response of a service trade to exchange rate changes, we found much more support for the hypothesis. Precisely, we found support for the asymmetric J‐curve in the US insurance (finance) trade with Australia, Belgium, France, and Korea (Australia, Germany) and asymmetric inverse J‐curve in the US insurance (finance) trade with Germany, Italy, and United Kingdom (Belgium, Canada).
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.002 | 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".