Perkembangan Makroekonomi Negara Kanada Dengan Analisa Vector Error Correction Model (VECM)
Bibliographic record
Abstract
This study aims to analyze the relationship between Gross Domestic Product (GDP), Inflation (INF), Import (IMP) and Unemployment (UEM) that occurred in Canada by using Vector Error Correction Model (VECM) analysis. The data source comes from https://www.imf.org; data taken from 1980 to 2020. The analytical tool used is the Vector Error Correction Model (VECM) which aims to analyze the relationship or causality between variables both in the short and long term, where the results obtained are the relationship between variables more referring to short term causality. And to find out the impact between variables, this study uses an Impulse Response Function (IRF) analysis where the results that have a positive impact during the Covid-19 pandemic are import shocks to GDP in Canada while the results that have a negative impact are import shocks on unemployment and also Canada's unemployment rate against GDP during the COVID-19 pandemic.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".