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Record W4390719782 · doi:10.26480/msmk.02.2023.103.113

APPLICATION OF DYNAMIC REGRESSION (DR) TO MODELING OF THE GROSS DOMESTIC PRODUCT (GDP) OF NIGERIA

2023· article· en· W4390719782 on OpenAlex
B. Biremo, Ibraheem Raji, Ilugbo S.O.

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMatrix Science Mathematic · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicStock Market Forecasting Methods
Canadian institutionsnot available
Fundersnot available
KeywordsGross domestic productStock exchangeEconomicsMarket capitalizationQuarter (Canadian coin)Stock marketRegression analysisEconometricsReal gross domestic productGross outputMacroeconomicsStatisticsMathematicsFinanceGeography

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.033
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.010
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.458
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it