External factors influence bank lending. Case of : Malaysia, India, Saudi Arabia And Indonesia / Wan Muhammad Hanif Wan Razalee
Bibliographic record
Abstract
The purpose of this study is to determine the factors that influence the bank lending in Malaysia, India, Saudi Arabia and Indonesia. There are three variables been selected as the independent variables which are interest rate (IR), Cash reserve requirement (CRR) and Exchange rate (ER) and Bank lending as the dependent variable (LD). Due to the unavailability data in Thomson One Reuters, only four countries that being selected in this study. There are several test will be run in this study to know the relationship between dependent variable and independent variables such as descriptive statistics, unit root test, regression using panel data and diagnostic test with the purpose to test hypothesis. The data was collected from 1th quarter 2006 until 4th quarter 2016. From the result, interest rate and cash reserve requirement shows negative and significant with the bank lending, meanwhile exchange rate has positive and significant with the bank lending. Even though all the independent variables shows significantly with dependent variables, but there are problem in this study which are diagnostic test and autocorrelation test. It means that the data is not normally distributed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".