The determinants of Malaysia trade balance/ Nurul Atikah Noraji
Bibliographic record
Abstract
The study focus on the analysis of the main determinants that may have an impact on trade balance. Specifically, this study aims to determine the main determinants of trade balance in Malaysia by analyzing the relationship of domestic income (GDP), inflation (CPI), exchange rate (ER), foreign direct investment (FDI), government expenditure (GEX) with household consumption expenditure (HCEX). The study employs quarter data from the 1998 until 2016 that will gather from the UiTM DataStream. This study will use E-views as a tool for analyzing the data that have been collected. Increase in exchange rate will influence the domestic price, with the devaluation, we expect to have positive sign. On the other hand, the rise of household consumption expenditure might due to the risen of the income tend to worsen the trade balance. Hence we expect to have negative sign. Next, as the net income increase then we expect more good will be imported so this will lead to help improving the trade balance. Therefore, we expect to have positive sign. The rise in government expenditure will make the trade balance become worse so we expect the negative sign for this variables. For the foreign direct investment, we expect to have positive sign and lastly for the inflation, when inflation is low usually it may have higher trade deficit due to the monetary policy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".