Bibliographic record
Abstract
Abstract To settle the theoretical and practical disputes over monetary policy cross-cycle adjustment, this paper explores the possible effects of China’s monetary policy cross-cycle adjustment based on empirical data. By using China’s macroeconomic data between the first quarter of 2000 and the fourth quarter of 2021, we use the HP filtering method to measure the trend of economic cyclical volatility, the three-stage SETAR model and the trend mutation point identification method to identify two types of cycles, respectively, and the FAVAR model to make empirical judgments on the effectiveness of monetary policy cross-cycle adjustment. We have the following research findings. First, monetary policy has certain cross-cycle adjustment effects on aggregate output, but has quite strong state dependence. Second, monetary policy has no cross-cycle adjustment effects on industrial output. Third, the higher the economic uncertainties, the worse the monetary policy cross-cycle adjustment effects, which, however, can be increased by intensifying monetary policy regulation. Fourth, in the economic recession stage, quantity-based monetary policy has advantage over price-based monetary policy in cross-cycle adjustments, while both of the above policies have no cross-cycle adjustment in the economic growth stage. Fifth, policy expectation plays an important role in cross-cycle adjustment, and reinforcing expectations is the key to realizing cross-cycle adjustment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".