Monetary Policy and Nigeria’s Economy: An Impact Investigation
Bibliographic record
Abstract
The major objective of this study is to empirically analyze the impact of monetary policy on the economy of Nigeria. To achieve this major objective, the study made use of broad money supply (M2) and credit to the private sector (CPS) as the independent variables explaining the dependent variable which is the Gross Domestic Product (GDP). The time series data employed cover the period of 1996 to 2016 and have been collected from the Central Bank of Nigeria Statistical Bulletin. The statistical tool used in this study is the multi regression and student t-test with the aid of statistical package for social sciences (SPSS) to analyze the impact of the individual explanatory variables on the economy. The result indicates that the monetary policy in Nigeria does not have significant impact on the economy. At 5% level of significance, the broad money supply (M2) is 0.36 > 0.05 while the CPS shows 0.22 > 0.05. The result proves that the broad money supply has not been properly regulated and the bank lending rate to the private sectors so high that the economy has been adversely affected. The study therefore, recommends that the Central Bank of Nigeria should put every machinery in place to ensure that the monetary policy is geared towards economic growth through substantial reduction of bank lending rate to the private sector and proper regulation of broad money supply.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".