Stability of the “returns–growth” relationship in G7: The dynamic conditional lagged correlation approach
Bibliographic record
Abstract
The relationship between stock market returns and real economic output has been studied in many empirical works over several decades. We present a simple methodology to verify the time-varying structure of this “returns–growth” relationship using dynamic conditional correlation model. Monthly stock market returns and output growth data for G7 countries from January 1961 to July 2013 are utilized. Our main findings can be summarized as follows: (i) the “returns–growth” relationship is positive and holds over the entire period for all G7 countries, (ii) the average correlations for the US and Canada were higher, and much lower for France and the UK, (iii) after the weakening of the “returns–growth” relationship during 80s and 90s, the correlations between stock market returns and output growth were higher, and (iv) for some countries within several sub-samples we also found evidence, that higher levels of correlation were accompanied with higher levels of market volatility.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".