Autoregressive Distributed Lag (ARDL) Analysis of Foreign Portfolio Investments Determination in Nigeria
Bibliographic record
Abstract
This study investigates the key macroeconomic variables determining foreign portfolio inflows (FPI) to Nigeria using the autoregressive distributed lag procedure that includes the bounds test of cointegration and error correction mechanism applied against time-series Nigerian data from 1986 through 2019. The results reveal the existence of long-run equilibrium relationship between FPI and exchange rate (EXR), inflation (INF), interest rate (INT), real GDP, and Tax (TXR). Short-run errors are adjusted at a speed of 77.87% per annum, in the long-run. Causality is found to jointly-flow from the explanatory variables to FPI inflows. In all the model estimations - autoregressive, short- and long-runs, exchange rate exerted negative and significant effect on FPIR. Inflation and tax significantly affected FPI inflows to Nigeria. Growth in real GDP and interest rate positively influenced FPIR, but not significantly. The results indicate that the major determinants of FPI inflows are exchange rates, inflation, and tax.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".