Stability of the “returns–growth” relationship in G7: The dynamic conditional lagged correlation approach
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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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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it