Empirical Modeling for the Spot Price of Gold Based on Influencing Factors
Bibliographic record
Abstract
In light of the special roles of the price of gold on the technological and economic development as well as social aspects of human society, it is of great importance and necessity to develop a series of statistical models that, based on sound reflection of the current structure of the gold market, are able to provide valuable references for trends of the gold price. In fact, the gold price is influenced by a variety of economic factors. For forecasting purposes, it is useful to study the short- and long-run effect of direct and indirect economic factors towards the supply and demand of gold and thus the spot price of gold. To this point, this paper focuses on analyzing the individual and mutual impact of influencing factors on the gold price and, simultaneously, providing a short-term forecast of the price of gold based on the relationships with other major macroeconomic variables. While these relationships are modeled with multiple regression, forecasts of individual factors are obtained under multivariate time series models. The forecasting results are found to be accurate in a relative sense, confirming the significant impact of the factors chosen while indicating the validity of the modeling idea applied.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".