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Record W2800147803 · doi:10.5539/ibr.v11n6p73

Influence of Real Exchange Rate and Volatility on FDI Inflow in Nigeria

2018· article· en· W2800147803 on OpenAlexvenueno aff
Saidu D Muhammad, Nnanna P. Azu, Ngozi F. Oko

Bibliographic record

VenueInternational Business Research · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsDevaluationEconomicsVolatility (finance)Foreign direct investmentAutoregressive conditional heteroskedasticityExchange rateMonetary economicsInflowVolatility swapInternational economicsEconometricsMacroeconomicsImplied volatility

Abstract

fetched live from OpenAlex

The purpose of this research is to ascertain the effect of real exchange rate fluctuation and its volatility on inward flow of FDI with Nigeria as a focal country, between 1970 to 2014. The research applied GARCH (1,1) to ascertain the level of volatility and ARDL model was used to determine the relevant results-these techniques were adopted for their robustness in estimation. It could be revealed that the effects of exchange rate and exchange rate volatility are more of a short-run phenomenon; while devaluation would increase inflow of FDI, volatility makes foreign investors more sceptical with increasing uncertainty. Increasing uncertainty could deter inflow of FDI into the country. Having captured the effect of political regime in the model, the paper reveals that a democratic regime should be the mainstay since it attracts more foreign investment compared to the military regimes. Therefore, even though devaluation is good, it would be better under civil government regimes.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.000
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 head0.063
GPT teacher head0.336
Teacher spread0.273 · 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.

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

Citations15
Published2018
Admission routes1
Has abstractyes

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