Bibliographic record
Abstract
Financial liberalization, coupled with increasing technological developments, has led to the ease of international capital flows, accelerated the circulation of information and thus enabled the integration of financial markets in different countries. This situation leads to the fact that positive or negative developments in one market affect other markets as well. This situation has led some financial indicators and credit rating agencies' ratings/reports to become more important, especially for investors. One of these is CDS (Credit Default Swap) rates. This study aims to examine the relationship between CDS rates and growth rates of G7 countries (Germany, United States, United Kingdom, France, Italy, Japan and Canada) and BRICS countries (Brazil, Russia, India, China and South Africa) classified according to their development levels. Annual data between 2008 and 2022 are used in the study. CDS (5-year USD-based bond yield) premium is used as the independent variable and GDP (Gross Domestic Product) % change is used as the dependent variable. In the analysis of the data, cross-section dependence, stationarity and homogeneity tests were conducted first. Horizontal cross-section dependence and heterogeneity were found to exist. Panel VAR and Panel Causality analyses were conducted. According to the test results, a causality relationship was found from CDS to GDP in the short and long run at the 1% significance level, while no causality relationship was found from GDP to CDS in the short and long run. As a result of the short-long run effects and causality tests, a high causality relationship was found between economic growth and CDS rates in Germany and the US among the G7 countries, while a high causality relationship was found in Russia and a lower causality relationship was found in China among the BRICS countries. As can be seen from the results, even in countries classified according to certain criteria, the relationship between CDS and economic growth does not have the same degree of impact. Further studies using different countries, different time periods and different analysis methods will contribute to the literature.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".