Modeling foreign direct investment returns and economic growth in Nigeria
Bibliographic record
Abstract
Foreign Direct Investment (FDI) is a strategy used by the majority of developing nations, including Nigeria, to increase their foreign exchange reserves through investments, business ventures, and international aid. The main goal of this study is to model foreign investment returns and economic growth in Nigeria. To my knowledge, no prior research on the relationship between FDI and economic growth has used a simulation with differential equations, therefore our study adds to the body of knowledge by using both a robust regression model and the simulation approach. The robust regression model was used, and the outcome demonstrates that foreign direct investment positively affects Nigeria's economic expansion. According to the simulation results, an additional $1 billion increase in foreign direct investment will cause Nigeria's GDP to grow by around $3 billion. Robust regression and simulation models combined for improved precision show that FDI has a beneficial impact on economic growth. Consequently, the Nigerian government must step up by creating a more favourable environment and ensuring the safety of people and property to draw in foreign investors. It must also increase the country's foreign reserves by investing enough money from the removal of subsidies to give average Nigerians access to financial inclusion, infrastructure growth to boost productivity, and more employment opportunities to make it easier to payment of foreign debt.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".