Factors Affecting on the Price of Gold on Global Markets and Its Impact on the Price of Gold in Iran Market (Incorporation of Dynamic System Pattern and Econometric)
Bibliographic record
Abstract
The financial crisis of 2008 caused that the gold price forecasting to be more important than was in the past. The mentioned importance is not just to earn more profits from gold speculative, but is because of the role that gold plays in the economic thermometer. In this paper, has tried to using the corporation of dynamic system patterns and econometric to be discussed a wide range of variables affecting the price of gold and in addition to analyzing the global gold price, study it’s impact on the gold price in Iran market. It seems that in Iran the exchange rate plays an important role in this regard. Also using dynamic simulation for a ten years period, means from 2015 to 2025 has forecasted the price of gold on global markets and Iran market. The results indicate a gradual decline in the gold price. The reality testing of model has examined by scenario plan of stopping the federal’s expansionary policies that the results indicate the validity of the model.
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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 | 0.001 |
| 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".