ARDL PANEL MODEL OF INTERNATIONAL FINANCIAL SYSTEM AND MONETARY POLICY OF ASIA PASIIFIC ECONOMIC COOPERATION
Bibliographic record
Abstract
The financial system plays an important role in the economy. An unstable financial system will be vulnerable to various problems that disrupt the rotation of a country's economy and be vulnerable to economic problems such as the global crisis in various countries. The problem that occurs is the occurrence of Covid-19 causing various fluctuations in the level of inflation, money supply, imports, the occurrence of unstable inflation from January 2019 to August 2021, low inflation resulting in a decrease in imports and an increase in the money supply in Mexico. , Vietnam, Philippines, Hongkong, Indonesia, Canada, Malaysia, Singapore, Peru, and China. The analytical method in this study uses the ARDL Panel (Autoregression Distributed Lag) approach. The ARDL Panel Model determines which country models from APEC countries are able to control long-term financial system-based economic fundamentals in Mexico, Vietnam, the Philippines, Hong Kong, Indonesia, Canada, Malaysia, Singapore, Peru, and China and the Different Test for modeling the impact of covid-19 19 on the economic fundamentals of the financial system. The results of the research found the ARDL Panel prediction model in modeling the impact of Covid-19 on economic fundamentals in the financial system. The main Leading Indicator of variable effectiveness in controlling Inflation In TAPEC is JUB where Vietnam, the Philippines, Hong Kong, Japan, Malaysia, Singapore, Peru and China have a significant influence in controlling Inflation. Then overall in the long term (Long Run) it turns out that only the JUB and CDV variables have an effect on INF In TAPEC, while in the short term (Short Run) it is JUB that influences Inflation In TAPEC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".