ANALISIS STABILITAS KEUANGAN GLOBAL 8 NEGARA TERPILIH (PENDEKATAN PANEL REGRESSION)
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui dampak terjadinya krisis ekonomi global pada 8 negara terpilih yaitu Indonesia, Hongkong, USA, Australia, Brazil, Kanada, India dan Jepang. Variabel dalam penelitian ini adalah Suku Bunga, Inflasi, Transaksi Berjalan, Cadangan Devisa, Foreign Direct Investment (FDI), Nilai Tukar dan Saham. Indikator pada penelitian ini dipilih indeks saham JKSE (Jakarta Composite Index) yang mewakili bursa saham Indonesia, HSI (Hang Seng Index) yang mewakili bursa saham Hong Kong, DJI (Dow Jones Industrial Average) yang mewakili bursa saham Amerika Serikat, AORD (All Ordinaries) yang mewakili bursa saham Australia, BVSP (Bovespa Sao Paoloo) mewakili bursa saham Brazil, TSX (Toronto Stock Exchange) mewakili bursa saham Kanada dan BSESN (Bombay Stock Exchange) mewakili bursa saham India. Metode analisis yang digunakan dalam penelitian ini adalah menggunakan model Panel Regression. Hasil penelitian Panel Regression diketahui bahwa nilai prob t statistic Suku Bunga dan Cadangan Devisa berpengaruh signifikan terhadap Nilai Tukar, sedangkan nilai prob t statistic pada variable saham menjelaskan bahwa Suku Bunga, Cadangan Devisa dan Foreign Direct Investment berpengaruh signifikan terhadap Saham negara Indonesia, Hongkong, USA, Australia, Brazil, Kanada, India dan Jepang. Hal ini disebabkan lemahnya fundamental ekonomi suatu Negara serta nilai suku bunga domestik di Indonesia sangat terkait dengan tingkat suku bunga internasional dimana akses pasar keuangan domestik terhadap pasar keuangan internasional serta kebijakan nilai tukar mata uang yang kurang fleksibel.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".