Does Export Lead Economic Growth? Or Other Way? VEC-Granger Causality Evidence from Nine South-East Asian Countries
Bibliographic record
Abstract
Applying the Vector Error Correction (VEC) model and the VEC Granger causality/Wald Exogeniety tests, this paper investigated the causal relation between export, economic growth, and financial development of nine South and South East Asian countries during 1974-2015. The significance of the error correction term (ECT) established short and long run dynamics. The VEC Granger causality/Wald Exogeniety tests found bidirectional Granger causality between economic growth (GDP) and export (EXPRT) in Malaysia, Singapore, and Thailand. Unidirectional causality running from EXPRT to GDP was found in Bangladesh, Pakistan and Sri Lanka. Unidirectional causality running from GDP growth to export was found in India. Bidirectional Granger causality between financial development (BKCRDT) and export growth was found in Thailand. Pairwise Granger causality results, because of lack of cointegration, found that GDP Granger caused EXPRT in Indonesia. The paper provides policy prescription that the governments should provide emphasis on promoting andprotecting the export industries that promotes the economic growth of the countries
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".