Gold Price and GDP Analysis of the World’s Top Economies
Bibliographic record
Abstract
The study examines the relationship between gold prices and real GDPs of the world’s largest gold-holding economies, which include the United States, the United Kingdom, France, Germany, Italy, Brazil, Japan, Europe, and Canada, for the 16-year period from December 1995 to June 2011. The authors calculate the multiple correlation coefficient and coefficient of determination to study this relationship. The multiple correlation coefficient measures the relationship between the gold price and GDP based on the regression equation, while the coefficient of determination indicates the percentage of the variation in gold prices that can be explained and accounted for by the GDPs of the world’s largest gold-holding economies in the regression equation. The authors perform multiple regression analyses to study the effect of nine GDPs on the movement of gold price. Results imply that the GDPs of seven out of nine economies when used together best predict the movements in the gold price. They also find that when individual GDPs are regressed with gold prices, the gold price is least correlated with Italy’s GDP but highly correlated with Brazil’s GDP. The gold price is only moderately correlated with the U.S. GDP even though the United States has the world’s highest gold holdings. TOPICS:Global, commodities, portfolio construction
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".