Stock Market Cycle and Business Cycle in China: Evidence from a Bootstrap Rolling Window Approach
Bibliographic record
Abstract
This paper investigates the causality between the stock market cycle and business cycle in China using the bootstrap full-sample causality test and sub-sample rolling-window causality test. The full-sample causality test suggests a unidirectional causality from the stock market cycle to the business cycle in China. However, we find the parameters in the VAR models consisting of the full-sample data are unstable by conducting a parameter stability test. This implies that the results from the full-sample causality test cannot be relied upon. Consequently, we turn to employ a bootstrap rolling window approach which can identify the time-varying feature in the causality. Using a 24-quarter window size, we do find that the bi-directional causality between the stock market cycle and business cycle in China does exhibit substantial time variations. Moreover, the causal effect of the stock market cycle on the business cycle is much weaker than that of the business cycle on the stock market cycle. In other words, stock market volatility is not the main factor that affects the business cycle formation and development in China. These findings have important implications for policy makers and investors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".