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Record W2064473139 · doi:10.12735/jfe.v1i1p10

Pure Portfolio Approach to Money Supply Determination in Nigeria: A Generalized Method of Moments Approach

2013· article· en· W2064473139 on OpenAlexvenueno aff
Ernest Simeon Odior

Bibliographic record

VenueJournal of Finance & Economics · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsPortfolioGeneralized method of momentsMoney supplyEconomicsEconometricsFinancial economicsMonetary economicsMonetary policy

Abstract

fetched live from OpenAlex

This study analyses broad money supply in Nigeria using a pure portfolio approach in order to establish an econometric framework which forecasts the Nigerian money multiplier with great precision. Methodologically, the Generalized Method of Moments (GMM) model was modeled to analysis the nature of the framework, where broad money supply is presumed to depend upon changes in various indicators of supply of money and a list of instrumental variables (IV) which were estimated over the period 1970-2010. Integral to this process is to determine if there exist a stable relationship between various measures of money supply, the monetary base and the instrumental variables, given a switch by the Central Bank from a direct to an indirect policy regime. In the results, it was found that there exist partial stable relations between these measures of money supply: the broad money and base money despite regime shifts over the sample period. However, a stable money multiplier was not found. This approach produced a scientific framework that could be used to predict the money multiplier derived from the broad money and could be used to forecast on an annual basis with reasonable accuracy at least in the medium term and projections in the monetary programme.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.045
GPT teacher head0.246
Teacher spread0.201 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2013
Admission routes1
Has abstractyes

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