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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".