Understanding the Interaction of Chinese Fiscal and Monetary Policy
Bibliographic record
Abstract
Interaction of fiscal and monetary policy is crucial for macroeconomic stability, especially for an economy with downward pressure as well as a tightened space for macro policy, like China. In this paper, we use a time-varying-parameter (TVP-VAR) model to study Chinese fiscal–monetary interaction and divide it into three periods. We claim that China went through a monetary dominant regime from 1996Q to 2017Q4 since the response of CPI to a fiscal expansion was negative in the short run and about zero in the long run, while the monetary expansion had positive effects on CPI. During this period, the response of government spending and money supply to each other’s shock had the same sign, indicating that the two policies acted as complements. However, we argue that 2008Q4 was a turning point that divided this period into two different periods. The response level of M2 growth rate to a fiscal expansion kept rising from 1996Q1 to 2008Q4, indicating the central bank’s increasingly active cooperation with fiscal policy, while it decreased from 2009Q1 to 2017Q4. Since 2018Q1, the economy has been going through a fiscal dominant regime in that the response of GDP growth rate and CPI to the fiscal expansion has sharply increased. We also argue that the relative change of the role between the two policies should be mainly attributed to the variation in the fiscal authority’s characteristics because fiscal response to a monetary shock has remained at a similar level the whole time, even if there have been changes in the characteristics of the central bank.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".