Modeling Long-Run and Short-Run Dynamics of Foreign Direct Investment on the Manufacturing Sector Growth in Nigeria: The ARDL Bound Testing Approach
Bibliographic record
Abstract
This paper econometrically examines the long run and the short run dynamics of foreign direct investment (FDI) on the manufacturing sector growth in Nigeria between the period 1981 and 2015. Data used in this study were obtained from the Central Bank of Nigeria statistical bulletin published in 2016. The econometric methodology adopted was the bound test and auto regressive distributive lag (ARDL) approach to estimate cointegrating relationship as well as short run and long run dynamics of the FDI and other explanatory variables on output growth in the manufacturing sector. Results of the long run behaviour and short run dynamics (error correction model) indicate that economic liberalization is significant in influencing changes in manufacturing output growth. However, FDI has no significant effect in both the short run and the long run episode. Therefore, it is recommended that policies aimed at encouraging increased participation of private domestic investors in collaboration with multinational corporations in the manufacturing sector be crafted.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 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".