Impacts of banking sector on the Chinese economy : research on the monetary transmission mechanism
Bibliographic record
Abstract
Researches on impacts of the banking sector on economic performance are not only provided for those developed economies such as the United Kingdom, the United States, Germany, and Japan, but also for those developing economies, such as South American, Asian and Eastern European countries. In this research, empirical approach has been adopted to explain the monetary transmission mechanism to document the characteristics of the bank lending channel in China since her implementation of the open-door policy. We study how bank loans are transmitted into changes in the economy reflected by variables such as real GDP and inflation. Furthermore, the key economic variable of aggregate investment is decomposed into domestic investment and foreign direct investment in the bank lending channel to study their relationship. Our research comprises two sets of data: first, aggregate time-series data from 1994 Quarter 1 to 2002 Quarter 3 with emphasis on recent economic performance of China and second, unbalanced annual panel data from 1978 to 2002 of provinces are categorized into different regional blocks. Inter-regional comparison is followed by the Granger causality tests. It is found that these two approaches of using the aggregate time -series and panel vector autoregressive (VAR) models give quite different results. The favored panel VAR model provides rich dynamic results which strongly support the hypothesis of multi-directional causality cycle in bank lending channel for China. Also results of causality tests are varied across different regions. The study concludes by with addressing the main issues and policy implications behind the findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".