Persistence, Long Memory, and Unit Roots in Commodity Prices
Bibliographic record
Abstract
This article investigates the degree of persistence in several weekly and monthly agricultural prices (corn, soybeans, barrow and gilts, and milk) using long memory (fractional integration) techniques. The results indicate mean reversion (i.e., orders of integration smaller than one) in some of the agricultural prices like corn, milk, and barrow and gilts when the disturbances are autocorrelated. Further, we examine the stability across time in the degree of dependence, and the results indicate that the fractional differencing parameters have not remained constant across time. When we take into account a structural break we find that during the first subsamples, the series are stationary though highly persistent, with orders of integration close to 0 and with large autoregressive coefficients. However, for the periods after the break, the series seem to be nonstationary I(1). Dans le présent article, nous avons étudié le degré de persistance des prix hebdomadaires et mensuels de plusieurs produits agricoles (maïs, soja, castrats et cochettes, lait) à l’aide de tests de mémoire longue (intégration fractionnaire). Nos résultats indiquent une stationnarité (c.‐à.‐d. des ordres d’intégration inférieurs à un) des prix de certains produits agricoles, tels que le maïs, le lait, les castrats et cochettes, lorsque les perturbations sont autocorrélées. Nous avons également étudié la stabilité du degré de dépendance à travers le temps, et nos résultats indiquent que les paramètres de différenciation fractionnaire ne sont pas demeurés constants. Lorsque nous avons tenu compte d’une rupture structurelle, nous avons trouvé que dans les premiers sous‐échantillons, les séries étaient stationnaires quoique très persistantes, avec des ordres d’intégration près de 0 et d’importants coefficients d’autorégression. Dans le cas des périodes suivant la rupture, les séries semblaient non stationnaires I(1).
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".