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Record W4401461508 · doi:10.33429/cjas.01024.2/7

External vulnerability and optimal monetary policy in Nigeria

2024· article· en· W4401461508 on OpenAlexaff
Sunday Oladunni

Bibliographic record

VenueCentral Bank of Nigeria Journal of Applied Statistics · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsTreasury Board of Canada Secretariat
Fundersnot available
KeywordsVulnerability (computing)Monetary policyEconomicsMonetary economicsComputer scienceComputer security

Abstract

fetched live from OpenAlex

In this study, we assess the external vulnerability of the Nigerian economy by documenting three alternatives (zero, partial and full) oil price pass-through to inflation within a New Keynesian Dynamic Stochastic General Equilibrium (DSGE) framework. The results show that under a zero-oil price pass-through, the choice of inflation measure is immaterial, as macroeconomic responses to the shock are comparable under alternative Taylor rule specifications. The shock precipitates stagflation, transmitted via the income and exchange rate channels; and introduces an extra layer of vulnerability associated with higher external risk premium. Both core and oil inflation targeting monetary rules maximize welfare under a zero-oil price passthrough, while oil inflation targeting is shown to be welfare superior under partial or full oil price pass-through. Credibility consideration renders core inflation targeting the feasible optimal path for the central bank.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.247
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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