Revisiting Foreign Direct Investment, Stock Market Capitalization, and Economic Growth between G7 and BRICS Countries: Using Bootstrap ARDL Test for Cointegration
Bibliographic record
Abstract
[[abstract]]This paper intends to investigate foreign direct investment, stock market capitalization, and economic growth for the BRICS countries and G7 countries by using a newly bootstrap autoregressive distributed lag (ARDL) test proposed by McNown, Sam, and Goh (2016) to examine whether the related variables have long-term equilibrium relationship. Economic growth is the dependent variable for Canada is found cointegrated relationship. Degenerate cases #2 are found in South Africa, Germany and Japan. On the other hand, the results of test with foreign direct investment on the stock market capitalization show that foreign direct investment positively or negatively Granger-causes stock market capitalization in Russia Federation (-), India (+), China (+), France (-), Canada (+) and UK (+), and the results of test with economic growth on the stock market capitalization show that economic growth positively or negatively Granger-causes stock market capitalization in Russia Federation (+), India (+), Canada (+), USA (+), Japan (-).
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".