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Record W1607695324 · doi:10.5539/ass.v11n15p113

A Study on Mapping Out Alliance Between Economic Growth and Foreign Direct Investment in Pakistan

2015· article· en· W1607695324 on OpenAlexvenueno aff
Khawaja Asif Mehmood, Sallahuddin Hassan

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsForeign direct investmentEconomicsGranger causalityUnit rootUnit root testAugmented Dickey–Fuller testCausality (physics)Monetary economicsMacroeconomicsInternational economicsEconometricsCointegration

Abstract

fetched live from OpenAlex

Sustained economic growth is a trance of all the developing and developed countries of the world. The need isnot just a fetch of economic growth rather is a realization of a fact that the why some economies that receiveheavy amount of foreign capital inflows in term of foreign direct investment (FDI) still find hard to capture theeconomic growth targets. This research captures for the state of the position of economic growth in terms of FDIand other internal factors in Pakistan. The study is based on the time series analysis covering the range of datafrom 1972 to 2014. Johansen Juselius technique of co-integration is employed for the precise statistical findings.Unit root test is computed in terms of Augmented Dickey Fuller Test (ADF). Granger Causality test and ErrorCorrection Model (ECM) is employed to test for the short-run and long-run relationships and causality betweenthe variables selected in the equation of growth. The results of the study show that FDI and GDP possesspositive association in short-run as well as in long-run. Unidirectional causality is also found on account of FDIand GDP. The study suggests that the government of Pakistan is to further pave off the ways that it alreadypractices to attract FDI that is prerequisite for sovereign upcoming scenery of the country.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.094
GPT teacher head0.303
Teacher spread0.209 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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