Macroprudential Policy, Monetary Policy and Banking Sector Performance in Nigeria
Bibliographic record
Abstract
This study examined the impact of macroprudential policy, monetary policy and banking sector performance in Nigeria. This study used ex post facto research design and secondary data from Quarter 1 2007 to Quarter 4 2022. Data were sourced from the International Monetary Fund, Central Bank of Nigeria, and World Bank Database. The study utilized ARDL-bound testing and ARDL-ECM to estimate the data. It was found that in the long run macro-prudential policy Capital Adequacy and Liquidity (Liquid Assets to Short-term Liabilities) have a positive relationship with banking sector performance in Nigeria. More so, Liquidity (Liquid Assets to Total Assets) and Asset Quality have a negative and significant relationship with banking sector performance in Nigeria. In addition, in the short-run, monetary policy tools were more effective and it was found that Exchange Rate and Monetary Policy Rates have a negative and significant relationship with banking sector performance in Nigeria, while Money Supply has a positive relationship with banking sector performance in Nigeria. It was concluded that macroprudential policy tends to be more effective on banking sector performance in Nigeria in the long run, while monetary policy tends to be effective in the short run. Hence, both policies complement each other rather than substitute in mitigating risks Inherent in banking sectors.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".