Business Cycle Asymmetry in China: Evidence from Friedman's Plucking Model
Bibliographic record
Abstract
Abstract Friedman's plucking model of business fluctuations suggests that output cannot exceed an upper limit, but it is occasionally “plucked” downward below trends as a result of economic recessions. This paper investigates China's business fluctuations using quarterly real GDP data for the period 1978–2009. Our results show some evidence supporting Friedman's plucking model. We find that a ceiling effect of real output exists, and that negative asymmetric shocks significantly affect the transitory component, which captures the plucking downward behavior during the recession. The results also suggest that the basic asymmetric unobserved component model is not appropriate for directly modeling China's real output because the business cycle is inaccurately measured, but it works quite well when considering a structural break in the second quarter of 1992. The results reveal that although China's economy strengthened in the second quarter of 2009, it is essential for China's government to take further positive and effective measures to maintain sustainable development of the economy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".