EFFEKTIFIAS KEBIJAKAN MONETER, INFLATION TARGETING TERHADAP SHOCK PANDEMI COVID-19 : VAR ANALISIS
Bibliographic record
Abstract
Penelitian ini fokus pada analisis mengenai reaksi kebijakan moneter yang dilakukan terhadap shock yang disebabkan oleh pandemi Covid-19. Kanada merupakan salah satu negara pelopor inflation targeting. Apakah kebijakan moneter dalam melakukan penargetan inflasi akan membuat perbedaan saat menghadapi shock pandemi?. Data yang digunakan adalah time series. Data diperoleh dari situs bank sentral Kanada (Bank of Canada) dengan variabel-variabelnya adalah suku bunga, inflasi dan gap output. Runtun waktu dari tahun 1993Q1 sampai dengan tahun 2021Q3. Estimasi menggunakan metode Vector Auto Regression (VAR), mengacu pada variabel-variabel yang terdapat di Taylor Rule. Analisis menyimpulkan bahwa Bank sentral melakukan kebijakan moneter untuk merespon shock perekonomian dengan aneka guncangan yang terjadi dari output (GDP), inflasi dan suku bunga. Kebijakan moneter bank sentral dalam hal ini adalah mengatasi guncangan output yang berakibat terhadap inflasi dikendalikan dengan cara mengendalikan suku bunga untuk terciptanya kestabilan moneter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".