A Comparative Empirical Study on the Macroeconomic Objectives and Effectiveness of Interest Rate Policies of China and U.S.
Bibliographic record
Abstract
This paper summarizes the relevant literatures about the economic effects of interest rate policy and Taylor rules, on the basis of which macroeconomic objectives indicators of China’s and U.S.’s interest rate policies are come up with. Then the macro-economic objectives and effectiveness of their interest rate adjustment policies are studied through Granger causality test and multivariate co-integration regression model. The result shows: Firstly, both of China and U.S. take economic growth, price level and employment level into consideration when adjust the benchmark interest rates. China’s adjustment pays more attention to stable price level and promoting employment level, while U.S.’s adjustment focuses more on stimulating economic growth and promoting employment level. Secondly, compared with China, U.S.’s market-oriented interest rate adjustment mechanism is more effective to reflect and respond to changes in the macroeconomic situations. Thirdly, the effectiveness of U.S.’s adjustment is obviously superior to China’s. U.S.’s benchmark interest rate adjustment can significantly affect the economic growth and employment level, and price level to some extent as well; while China’s effectiveness is confined to price level. Therefore, China should accelerate the interest rate liberalization and learn from U.S. to reform the function mechanism of interest rate policy, so as to enhance the interest rate policy on the sensitivity and effectiveness of macro-economy.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".