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.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".