Financial development and economic growth: An application of ARDL model on developed and developing countries
Bibliographic record
Abstract
Financial development and economic growth are vital for both developed and developing nations because it determines a country’s economic growth rate and the complexity of its financial system. A sample of developed and developing economies from 2005-2019 was used in this study to observe whether there is an association between financial development and economic growth. The purpose of this study is to compare the association between financial development and economic growth in developed and developing countries. An ARDL model was used to analyze secondary data from the top 15 Developed countries (United States America, Japan, Germany, United Kingdom, France, Italy, Canada, Korea, Rep., Australia, Spain, Netherlands, Switzerland, Poland, Belgium, Austria) and top 15 Developing countries (China, India, Brazil, Russian Federation, Mexico, Indonesia, Thailand, Nigeria, Argentina, Philippines, Malaysia, South Africa, Colombia, Egypt, Arab Rep., Pakistan) based on GDP 2019 (Current US$). Findings of the study shows some interesting insights for long and short run relationship among study indicators based on ARDL model. This research might help developed countries to enhance their financial structure and economic growth rate over time, while for developing countries suggests new policies initiatives.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".