The Impact of Macroprudential and Monetary Policies Instruments on the Private Credit Growth in the Arab Banking Sector
Bibliographic record
Abstract
This paper investigates the potential impact of the macroprudential instruments, namely debt-to-income (DTI) ratios, the loan-to-value (LTV) on controlling the private credit growth in the Arab banking system, and we also attempt to examine the effects of the monetary policy instruments on private credit growth by using Generalized Method of Moments (GMM) technique. We measure the effect of loosening or tightening these instruments on the growth of the private credit using a sample covers ten Arab countries based on quarterly data for the period (2014-2019). The results reveal that the macroprudential policy tools have the power to control the private credit growth, as the effects of tightening the DTI and the LTV ratios appear directly after one quarter, while the change of the monetary policy tools and the required reserve ratio have a negative impact on the private credit growth, and their effects appear after two quarters and one quarter respectively. Finally, the results show that there is no evidence of significant impact of the economic variables on credit growth.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".