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Record W1980467606 · doi:10.5539/ijef.v5n6p91

The Macroeconomic Determinants of Stock Market Development in Jordan

2013· article· en· W1980467606 on OpenAlexvenueno aff
Hasan Mohammed El-Nader, Ahmad Diab Alraimony

Bibliographic record

VenueInternational Journal of Economics and Finance · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsCointegrationEconomicsEconometricsVariance decomposition of forecast errorsGross domestic productStock marketMultivariate statisticsUnit rootRelative priceCapitalizationStock (firearms)Market capitalizationMonetary economicsMacroeconomicsMathematicsStatistics

Abstract

fetched live from OpenAlex

This study examines the causes of stock market development in Jordan. The study uses monthly data between 1990 and 2011. The data is tested for stationarity by employing unit root tests. Results confirm that all variables are stationary, enabling us to continue in the modeling process. To achieve this objective, a multivariate cointegration and variance decomposition analysis are applied to examine the impact of these sources. The estimated findings demonstrate that the variables namely; Money Supply relative to, Total Value Traded relative to , Gross Capital Formation relative to , Consumer Price Index (CPI), and Credit to private Sector relative to all have positive and considerable influences on stock market development. On the other hand, Nominal Gross Domestic Product and Net Remittances relative to have a negative impact. From the estimated VECM, the variance decompositions (VDC) have been simulated as a basis for inferences. The Johansen and Juselius’ multivariate cointegration and variance decompositions analysis also confirm the presence of both a long-term and short-term dynamic relationship between the Stock market capitalization relative to GDP and macroeconomic variables. In the light of these results, the paper provides some policy implications to Jordan.

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: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.467

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 head0.034
GPT teacher head0.231
Teacher spread0.197 · 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

Citations39
Published2013
Admission routes1
Has abstractyes

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