Impact of Macroeconomic and Demographic Variables on the Stock Market: Evidence from Tunisian Crisis
Bibliographic record
Abstract
This study aims to analyze the long-run as well as the short-run relationship between macroeconomic, demographic variables and the Tunisian stock market for the period subsequent to the financial crisis. Monthly data over the period 2008-2014 and ARDL model have been employed. Results indicate that the Tunisian stock market index, macroeconomic and demographic indicators are cointegrated and, therefore, a long-run relationship exists between them. The long-run coefficients suggest that budget deficit, inflation rate and number of unemployed graduates had a negative effect, otherwise, money supply and number of non-resident entries had positive effect on the Tunisian stock market. Moreover, results from the error correction model show that the Tunisian stock market index is influenced positively by money supply and second order difference of the number of unemployed graduated and negatively by first and second order difference of money supply, inflation rate, first order difference of number of non-resident entries and number of unemployment graduates.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".