Modelling the Long Run Determinants of Domestic Private Investment in Nigeria
Bibliographic record
Abstract
The paper seeks to model the long run determinants of domestic private investment in Nigeria over the period 1970 to 2010, employing advanced econometric technique of Auto-Regressive Distributed Lag (ARDL) bounds testing approach. Emanated from the estimated models are intriguing findings which showed clearly that difference exist between long and short run determinants. Public investment, real GDP, real interest rate, exchange rate, credit to the private sector, terms of trade, external debts and reforms dummy are the key long run determinants of domestic private investment while public investment, real GDP and terms of trade are statistically significant in the short run. The policy prescriptions are that necessary infrastructures to complement domestic private investment should be put in place; that external debts be reduced to the barest minimum and negative effects of external shocks engendered by foreign direct investment uncertainty and deficit terms of trade should be prevented altogether.
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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".