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Record W2766330495 · doi:10.5430/ijfr.v8n4p228

Foreign Direct Investment, Financial Development and Economic Growth Evidence from Saudi Arabia

2017· article· en· W2766330495 on OpenAlex
Najeeb Muhammad Nasir, Mohammed Ziaur Rehman, Nasir Ali

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Financial Research · 2017
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
FundersKing Saud University
KeywordsForeign direct investmentGranger causalityVector autoregressionEconomicsCausality (physics)Context (archaeology)Monetary economicsVariance decomposition of forecast errorsFinancial marketMacroeconomicsFinanceEconometrics

Abstract

fetched live from OpenAlex

This study is an effort to explain and establish a relationship among foreign direct investment, financial development and economic growth in Saudi Arabian context for the period of 1970 to 2015 by employing Vector Auto Regression (VAR) and modified Granger Casualty Models. The result of Johansen co-integration test illustrates that no long run co-integration can be established among the variables. VAR has established a link between economic growth, financial development and foreign direct investment. The Granger causality test also confirms that economic growth causes foreign direct investment and financial development which is a unidirectional causality running from economic growth towards foreign direct investment and financial development. No significant causality can be observed empirically between foreign direct investment and financial development. This feature can be attributed to the fact that Saudi Arabian economy is still heavily dependent on its oil resources which is the driving force behind growth. Impulse Response Function has been utilized in order to observe the response to the shocks among the variables.

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.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.702
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0030.001
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 head0.088
GPT teacher head0.347
Teacher spread0.259 · 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