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Record W2766767709 · doi:10.5539/ijef.v9n11p207

The Real Exchange Rate-Foreign Direct Investment Controversy in South Africa: An Application of ARDL Approach

2017· article· en· W2766767709 on OpenAlexvenueno aff
Ahmed Mohamed Dahir, Fuaziah Mahat, A.N. Bany‐Ariffin, Nazrul Hisyam Ab Razak

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsDistributed lagCointegrationEconomicsForeign direct investmentExchange rateOpenness to experienceShort runMonetary economicsGranger causalityEconometricsInvestment (military)Autoregressive modelCausality (physics)MacroeconomicsInternational economics

Abstract

fetched live from OpenAlex

This paper examines the relationship between real exchange rate and foreign direct investment. We apply autoregressive distributed lag (ARDL) bounds testing method to estimate short and long-run relationships between the series in South Africa over the period of 1987-2016. The results reveal long-run cointegration relationships among variables are confirmed, implying real exchange rate, domestic market size stimulate the foreign direct investment in the long run. Furthermore, there is significant Granger unidirectional causality foreign direct investment to real exchange rate in short and long run and from market size to trade openness in a short run. This finding further suggests that the exchange rate instability are likely to be substantially harmful to a positive effect of FDI and should be avoided in South Africa.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.226
Teacher spread0.202 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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