The evolving financial landscape: analyzing uncertainty, risks, and growth in G7 economies of the 21st century
Bibliographic record
Abstract
This study provides a comprehensive analysis of the financial markets in the 21'st century; focusing on the G7 countries: Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. The justification for this research originates from the significant role these markets plays in the global economy and the need to understand their complexities in relation to risk, uncertainty, and economic growth. The primary objective is to empiricaly investigate the dynamics and correlations of these aspects within the financial markets of the countries selected in this study. The study is based on secondary data spanning 12 years, from 2010 to 2021, covering all 7 countries, and making it a panel data analysis. Methodologically, the research employs various econometric models and techniques, including Ordinary Least Squares, OLS Robust, and fixed and random effects models. The empirical results suggest that the fixed effects model is the most suitable for this study, as confirmed by the Hausman test. According to this model, a 1% increase in stock market capitalization relative to GDP positively impacts GDP growth by 0.06. Furthermore, stock market value trades were found to have a positive correlation with economic growth. In contrast, stock price volatility and pension fund assets negatively impact economic growth. Notably, these findings diverge from some previous studies in the field. In conclusion, the research provides valuable insights into the relationship between financial markets and economic indicators in the G7 countries, thereby offering policy-makers a more nuanced understanding of how to foster economic growth while mitigating risks.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".