Influence of Real Exchange Rate and Volatility on FDI Inflow in Nigeria
Bibliographic record
Abstract
The purpose of this research is to ascertain the effect of real exchange rate fluctuation and its volatility on inward flow of FDI with Nigeria as a focal country, between 1970 to 2014. The research applied GARCH (1,1) to ascertain the level of volatility and ARDL model was used to determine the relevant results-these techniques were adopted for their robustness in estimation. It could be revealed that the effects of exchange rate and exchange rate volatility are more of a short-run phenomenon; while devaluation would increase inflow of FDI, volatility makes foreign investors more sceptical with increasing uncertainty. Increasing uncertainty could deter inflow of FDI into the country. Having captured the effect of political regime in the model, the paper reveals that a democratic regime should be the mainstay since it attracts more foreign investment compared to the military regimes. Therefore, even though devaluation is good, it would be better under civil government regimes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".