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Record W3091644773 · doi:10.5430/rwe.v11n5p388

How Effective Is the Monetary Policy on the Real Sector in Nigeria?

2020· article· en· W3091644773 on OpenAlexvenueno aff
Ebenezer O. Oladimeji, Ebenezer Bowale, Henry Okodua

Bibliographic record

VenueResearch in World Economy · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsMonetary policyCredit channelEconomicsMonetary economicsPublic sectorOrder (exchange)Interest rate channelAsset (computer security)Financial systemInflation targetingMacroeconomicsBusinessFinanceEconomy

Abstract

fetched live from OpenAlex

In the past few years, the real sector became an area of interest in scholarly and public intellectual discuss, towards a sustainable performance of the Nigerian economy. Successive governments also realized the need to diversify the economy from high dependence on oil into deepening the real sector, through monetary policy that allows more credit flow to the real sector. In a quest to reconcile the current state of the Nigerian real sector with the renewed efforts of the government and the monetary authority to revamp the sector, this study investigated the effectiveness of this process and reexamined the transmission channels, using a structural vector autoregressive econometric approach (SVAR). The results showed that the credit channel and asset price channel are the dominant monetary policy transmission channels to the real sector. However, there was a significant effect on the effectiveness of the transmission process, when credit risk was added to the model, as it revealed vital information about the behaviour of the banking system in response to monetary policy actions of the monetary authority, during the period of high credit risk/default risk. This study, therefore, recommends that monetary authorities should always consider the credit preference of the banking system and the order of transmission channels, before embarking on any monetary policy aimed at stimulating the real sector and other sectors of the economy.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.171
GPT teacher head0.311
Teacher spread0.139 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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