Bibliographic record
Abstract
This paper discusses the short-run and long-run dynamics of price for storable natural resource commodities. It deals with the interrelationships between price, inventory, and price volatility; the effects of inventory and producers’ operating flexibility on the dynamics of price in the short-run; and the evolution of the long-run equilibrium price. The paper describes a model that explains the dynamics of commodity prices based on demand for inventory and on threshold prices that would induce producers to expand or contract operations. The paper concludes that in the short-run, producers’ operating flexibility reduces price volatility when the spot price is higher than the threshold price which would cause expansion in the scale of operations. It further concludes that operating flexibility will raise price volatility when the spot price is lower than the threshold price which would result in a contraction of operations. It concludes that in the short run, commodity prices tend to revert to a short-run equilibrium price, whereas in the long run, they tend to revert to a long-run trend. The paper demonstrates the failure of currently used parametric models in describing the stochastic process of commodity prices, and suggests using non-parametric methods. It also recommends including the time trend in such a model. As an illustration the paper presents an analysis of the price processes for lumber and cotton.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".