Monetary Policy Shocks and Manufacturing Sector Output in Nigeria: A Structural Var-Approach
Bibliographic record
Abstract
Over the years, the manufacturing sector has responded relatively low compared to other sectors such as services, agriculture, crude petroleum and natural gas despite its huge potential and various policies proposed towards a growth in the sector. This study investigated the effect of monetary policy on the manufacturing sector output in Nigeria using a quarterly data from 1981 to 2015 employing the structural vector autoregressive(SVAR) framework. An eight variable SVAR for manufacturing sector output was employed. The study examined the effect of monetary policy using the four monetary transmission mechanism channels and included a fiscal policy variable as well as labor and capital in the model. The short-run SVAR showed that only monetary policy rate and money supply conformed to theory. The impulse response functions showed that all monetary variables as well as other variables with the exception of government expenditure conformed to economic theory. One major finding of the study is that the lending interest rate accounted for the biggest variance in the manufacturing contribution to gross domestic product as shown by the forecast error variance decomposition. The study recommends that the monetary authority should ensure that the lending interest rate to the manufacturing sector is within a single digit, accessible, affordable and sustainable so as to ensure a greater productivity in the sector
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".