The Macroeconomic Determinants of Stock Market Development in Jordan
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".