Uncovered Interest Rate Parity and Investment: A Tripartite Analysis of Nigeria, United States of America and China
Bibliographic record
Abstract
This study comparatively examined the validity of the theory of uncovered interest rate parity (UIP) for Nigeria and United States of America (USA) and for Nigeria and China, using USA and China as anchor countries respectively. The study also examined the impact of the theory (UIP) on investment in Nigeria. Using annual time series data spanning from 1980-2017, the pre-estimation test (Augmented Dickey-Fuller Unit root test) was conducted. Given that the variables were integrated of order one and order zero, Autoregressive Distributed lag bound testing approach (ARDL) and Toda- Yamamoto causality test were employed for analysis. The ARDL result indicates that there is no long run relationship between Nigeria and USA but there is a long run relationship between Nigeria and China. By implication, the theory of UIP does not hold between Nigeria and USA but between Nigeria and China, the theory of UIP holds. Also, the result of Toda-Yamamoto indicates that the theory of UIP positively and significantly impacts on investment in Nigeria. The study recommended that the government should strengthen her economic relationship especially with China so as to encourage more investments by China in Nigeria.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".