Foreign Private Capital, Economic Growth and Macroeconomic Indicators in Nigeria: An Empirical Framework
Bibliographic record
Abstract
The understanding of the determinants of capital flows and the major challenges its sudden surge and flight might pose is central to assessing its macroeconomic impact in an economy. Most developing and transition countries are attracting large inflows of foreign capital that could spur economic growth or have destabilizing effect on their economies if not well managed and streamlined. This however, has aroused concern over their potential effects on macroeconomic stability, competitiveness of the export sector, and external viability. The study examines the relationship existing among foreign private capital components (Foreign Direct Investment (FDI) and Foreign Portfolio Investment (FPI), economic growth (GDP) and some macroeconomic indicators; interest rate (INTR) and inflation rate (INF) as well as policy implications therefrom, using time series data from 1986-2008. A nonrestrictive Vector Autoregressive (VAR) model was developed, while restriction is imposed to identify the orthogonal (structural) components of the error terms - Structural Vector Autoregressive (SVAR). Analysis indicates that the response of the GDP to shocks from the NDI is not contemporaneous and this is applicable to the other variables. It is somewhat sluggish but returns faster to equilibrium compared to the response from the NNPI. Restricting the recursive Cholesky structural decomposition of the IRF, both in the short-run and long-run, the result indicates that the NNPI impacts on the GDP at the short-run, while the NDI does not. Also, the INTR was shown to impact on the NNPI in the short-run. Furthermore, in the long-run, the GDP responds more to the impact of the NNPI compared to the NDI, while the NDI responds to INTR, the NNPI does the same to INF. Policy frameworks on Foreign Private Investment should be encouraged for the promotion of economic development in Nigeria. Consequently, it is recommended that government should not discourage the flow of those foreign private capitals but be more vigilant about the nature and sources of the flows. This is very important in order to forestall their potential adverse impacts on key macroeconomic variables as well as economic growth in a situation of their sudden surge or flight.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".