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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".