APPLICATION OF DYNAMIC REGRESSION (DR) TO MODELING OF THE GROSS DOMESTIC PRODUCT (GDP) OF NIGERIA
Why this work is in the frame
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Bibliographic record
Abstract
This study examine the significant contribution of Nigeria Stock Market towards the Gross Domestic Product. The main objective of this work is to assess the level of Stock Market stability in Nigeria by applying Dynamic Regression to modeling of Gross Domestic Product (GDP) using the Quarterly Gross Domestic Product at 1990 constant basic prices of Nigeria from the first quarter of 1985 to the third quarter of 2013. Quarterly all Share Index of the Nigerian Stock Exchange from the first quarter of 1985 to the fourth quarter of 2013 and finally Quarterly Market Capitalization of the Nigerian Stock Exchange for the same period. It was observed that the relationship is statistically significant, which allows the stock market to have an impact on the Nigerian Economy. It was concluded that Government and Economic planner should take more advantage of statistical tools in studying the relationship between the GDP movement and the Stock Exchange.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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 it