The Use of the VEC Model to Study the Impact of the Third Party Payment on the Interest Market in China
Bibliographic record
Abstract
This paper aims to study the effects of cross section of Internet finance and mobile payment on the Internet financial deepening and the interest rate liberalization. Because more than 50% of online payment is completed by way of mobile payments and financial products in Internet finance also have such two ways as online transactions and mobile client transactions, the interest rate index on behalf of the Internet finance can be largely replaced by the return of financial products in it. The study of the relationship between return of common monetary fund in transactional Internet finance and quasi benchmark fund—SHIBOR helps to determine the effect of Internet mobile payment on Internet financial deepening and the interest rate liberalization. First, ensure the stability of time series data on SHIBOR, Yu’E Bao returns, payment of the third party with ADF test and Johansen co-integration test and then build the VEC model and perform T test and AR stationary test, then make response analysis on impulse function to describe the impact of endogenous variables. Finally, make analysis and risk prevention suggestions according to empirical results based on the market situation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".