Bootstrap Causality Tests Of The Relationship Between The Equity Markets Of The US And Other Developed Countries: Pre- And Post-September 11
Bibliographic record
Abstract
We analyse the causal relationship between the equity markets of the US and those of the UK, Japan, Germany, France, Canada and Australia based on leveraged bootstrap approach developed by Hacker and Hatemi-J (2005). This method overcomes problems of non-normalities and ARCH effects in the data. Using weekly MSCI price indices, we focus our investigation on the period 1998 to 2005 which we divided into two sub-periods to take into account the potential structural break arising from September 11. Our results show that before September 11, there was bi-directional causality between the US and Japan and between the US and Germany. In addition, there was also a uni-directional causality from the US to Canada and from the US to France. After September 11, the only causality was a unidirectional one from the US to Japan and from the UK to the US. Thus, after September 11, the US Granger-caused a fewer number of markets. This could imply that after September 11, the other markets became more efficient in responding to information transmitted from the US market.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".