Interrelation Between Capital Market Index And Economic Growth: Comparison Among United-States, England And Japan, By Using Granger Causality Test
Bibliographic record
Abstract
The purpose of this research is to examine the interrelationship between capital market index and economic growth among United-States of America, England and Japan. Capital market and economic growth are both key elements upon what a national economic development relies on. In order to find out whether capital market ndex and economic growth are interrelated each other, Granger Causality Test was employed, when Augmented Dickey Fuller Test was used to check the stationary of historical data. The variables of the study were S&P 100, FTSE 100, Nikkei 225, and GDPs. Quarterly data obtained from official trustful websites were used. The period of the study was from the premier quarter of 1987 to the last quarter of 2016. \nThe findings conclude that there is an interrelation between capital market index and economic growth, but the direction of causality between the two variables is different in each country. A unidirectional causality from capital market index to economic growth has observed in case of United-States of America. Feed-back causality between capital market index and economic growth has seen in England. and a unidirectional causality has examined between capital market index and economic growth in Japan. The result of the present study is hoping to give an acknowledgement about how far capital market contributes on development of nation through a prospering economic growth; and vice-versa.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".