The dynamic factor model: an application to stock market indexes
Bibliographic record
Abstract
The fallout from the collapse of the US mortgage market and the reversal of the housing boom in several important countries has turned out to be more profound and persistent than expected in 2007 and beginning of 2008. To examine the association among the main world stock markets from 2008 onwards, we consider the dynamic factor model in a Bayesian framework. The series considered were daily data for S and P500 ( US), Shanghai Comp Index (China), FTSE100 (UK), CAC40 (France), DAX ( Germany), S and P/TSX (Canada), Bovespa (Brazil), Merval (Argentina), Nikkei 225 (Japan) during the period from January 4th, 2008 to May 10th, 2010. We observe that there is a main factor explaining the financial crisis which was felt in all stock market indexes. The second factor is composed only by China and Japan, the Asian countries, and the third factor is associated with European countries, namely Britain, France and Germany.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".