Formulating and Estimating of Dynamic Nonlinear Model of Korea’s Bilateral Trade Balance
Bibliographic record
Abstract
This paper formulates and estimates the dynamic nonlinear trade model for Korea. We use monthly time series data for the period from 2000 to 2017. We employ EGARCH (1,1)-GED model which allows the positive and negative shocks to have asymmetric influences on volatility. The Johansen co-integration test is applied and finds the long run relationship among oil price, exchange rate and trade balance does exist. With respect to Indonesia as one of oil exporting countries, we find that an increase in oil prices leads to a declined trade balance as imports rise more than exports. Appreciation in IDR also leads to a declined trade balance as exports fall more than imports. For Korea as one of oil importing countries, an increase in oil prices leads to an improved trade balance as exports rise more than imports. Appreciation in KRW leads to a declined trade balance as exports fall more than imports. Oil price volatility reduces trade balance both in Indonesia and Korea. Oil price has negative effects on Indonesia’s trade balance and positive effects on Korea’s trade balance. Indonesian and Korean currency appreciation against US dollar have a negative impact on trade balance in Indonesia and Korea respectively. This information will contribute to Indonesian and Korean policy makers in making policies for their trade.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".