The J-Curve at Industry Level: Evidence from Malaysia-China Trade
Bibliographic record
Abstract
To investigate the response of real depreciation of ringgit on trade balance of Malaysia, researchers either employed trade data between Malaysia and the rest of the world or between Malaysia and each of her trading partners. Nevertheless, these studies did not provide a conclusive evidence of the effects of currency depreciation on the trade balance, particularly in the case of Malaysia with China. This paper considers 53 industries and investigates the short-run (J-curve pattern) and the long-run effects of the real depreciation of ringgit/yuan on the trade balance of each industry. We use quarterly data over the period of 1993Q1- 2009Q4. The results from bounds testing approach and error-correction modelling indicate that whilst depreciation of ringgit has short-run significant effects on the trade balance in majority of the industries, the short-run effects translate into the favorable long-run effects only in 11 of the 53 industries. The results also reveal that J-Curve phenomenon exists only in 10 industries.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".