Bibliographic record
Abstract
Studi ini menganalisis pengaruh nilai tukar Rupiah terhadap kinerja ekspor Indonesia menggunakan data tahun 2005 kuartal I sampai tahun 2012 kuartal III dengan menggunakan Error Correction Model (ECM). Dalam kurun waktu 2005-2012 ekspor Indonesia secara umum menunjukkan perkembangan yang positif walaupun terjadi penurunan pada periode 2008-2009 dan tahun 2012 terutama ke negara-negara tujuan Eropa dan Amerika. Ini menunjukkan bahwa ekspor Indonesia perlu ditujukan ke negara negara yang menjadi target atau sasaran baru. Studi ini menemukan bahwa nilai tukar dalam jangka panjang dan jangka pendek memiliki pengaruh yang negatif dan signifikan terhadap ekspor Indonesia. Ini menunjukkan pentingnya kebijakan nilai tukar untuk memicu peningkatan ekspor Indonesia. This paper examines the influence of Indonesia’s exchange rate on the performance of Indonesia’s exports using the data from the first quarter of 2005 until the third quarter of 2012 using an Error Correction Model (ECM). During 2005-2012 Indonesia’s exports increased, except in 2008-2009 and 2012 when they declined especially to Europe and America. This suggests that Indonesia’s exports should now be directed at newly targeted countries. This study finds that the appreciation of the exchange rate, in both the long run and the short run, has a significant negative impact on exports. It shows theimportance of exchange rate policy in improving Indonesia’s export performance.
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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) | 0.000 | 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 | 0.001 |
| 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".