Latin American Stock Market Interdependence: A Dynamic Fluctuation Across Time
Bibliographic record
Abstract
Modeling the U.S.’s, Canada’s and several Latin American countries’ market index returns with an auto-regressive time series model estimated by equation-by-equation OLS, this paper focuses on the significant estimate coefficients which represent the influence that the countries modeled have over each other across various subperiods throughout the past two decades. The countries in the American continent have experienced many historical economic shocks, most being worldwide events, which have changed their economic relations and support. Focusing on Argentina, Brazil, Canada, Chile, Colombia, Mexico, Peru, United States and Uruguay, I find that overall, most countries tend to be independent but do have periods of time where they develop an interdependence with another countries index returns. In addition, Colombia is consistently influenced by others and the United States does not have as big of an impact over others as one would intuitively expect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.099 | 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 teacher head, 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".